Multilevel Thresholding According to Histogram

Multilevel Thresholding According to Histogram Make Multilevel Thresholding According to Histogram by Cooperative Algorithm based on AFSA and Fuzzy Logic Image segmentation is a technique which is usually applied in the first step of image analysis and pattern recognition and is an important component of them. This technique is taken into account as one of the most difficult and the most sensitive problems in image analyzing. In this paper, a cooperative algorithm is proposed based on AFSA and k-means. The proposed algorithm is used to make multilevel thresholding for image segmentation according to histogram. In the proposed algorithm, first, artificial fish (AF) perform optimization process in AFSA. After swarm convergence, obtained cluster centers by AFs are used as initial cluster centers of k-means algorithm. After forwarding AFSAs output to k-means, AFs are reinitialized and performs clustering again. The proposed algorithm is used for segmenting 2 well-known images and obtained results are compared with each other. Experimental results show that segmented images quality by the proposed algorithm is much better than four other t ested algorithms. Keywords: Multilevel Thresholding; Histogram; Cooperative Algorithm; k-means. Image segmentation is a technique which is usually applied in the first step of image analysis and pattern recognition and is an important component of them. This technique is taken into account as one of the most difficult and the most sensitive problems in image analyzing. In fact, quality of final result of image analysis depends highly on the quality of image segmentation result. In image segmentation process, an image is divided into different regions. Segmentation approaches of mono-color images are with respect to discontinuity and/or similarity of gray level amounts in one region. If the approach performs segmentation based on discontinuities, the image is segmented with respect to abrupt changes on gray level by means of recognizing dots, lines and edges [1].The purpose of image segmentation approaches is to classify and convert pixels into regions. Histogram thresholding is one of the techniques, which has been applied extensively in mono-color images segmentation [2]. Generally, images are composed of regions with various gray levels. Therefore, an images histogram can consist of some peaks that each of them is related to one region. To separate boundaries of two peaks from each other, a threshold value is considered between valleys of two adjacent peaks. Indeed, histogram thresholding is a famous technique which is looking for peaks and valleys in a histogram [3]. Various clustering algorithms such as k-means [4] and FCM [5] have been used for histogram thresholding so far. As a matter of fact, clustering approaches, because of simplicity and effectiveness, belong to the most famous techniques that could be used for natural image segmentation. Applying clustering algorithms in histogram thresholding are such that first colors histogram is built and after that, clustering is done according to color distribution among pixels. O ne of the clustering methods is to use such swarm intelligence algorithms as particle swarm optimization (PSO) [6], and artificial fish swarm algorithm (AFSA) [7]. PSO was presented by Kenedy and Eberhart in 1995 [8]. Different versions of this algorithm have been used many times in data clustering [9]. Artificial fish swarm algorithm (AFSA) was presented by Li Xiao Lei in 2002 [10]. This algorithm is a technique based on swarm behaviors that was inspired from social behaviors of fish swarm in nature. AFSA works based on population, random search and behaviorism. This algorithm has been applied on different problems including machine learning [11, 12, 13], PID controlling [14], image segmentation [16], data clustering [7, 16] and scheduling [17]. K-means or famous Lloyd algorithm is one of the famous data clustering algorithms [18]. This algorithm is of high convergence rate, but has some weaknesses such as sensitivity to initial values of cluster centers and convergence to local op tima. Researchers have tried to remove these weaknesses by hybridizing this algorithm with other algorithms such as swarm intelligence ones [6, 19] and to utilize their advantages. One of these algorithms is KPSO in which first, k-means is performed and after that outcome of k-means is delivered to PSO as a particle [20]. Hence, at the beginning of the algorithm, k-means reaches to a local optimum with its high convergence rate and after that PSO takes the responsibility of increasing the result accuracy and exiting form local optimum. In this paper, a cooperative algorithm is proposed based on AFSA and k-means. The proposed algorithm is used to make multilevel thresholding for image segmentation according to histogram. In the proposed algorithm, first, artificial fish (AF) perform optimization process in AFSA. After swarm convergence, obtained cluster centers by AFs are used as initial cluster centers of k-means algorithm. After forwarding AFSAs output to k- means, AFs are reinitialized and performs clustering again. In fact, in the proposed algorithm, AFSA is used for a global search and k-means is used for a local search. The proposed algorithm along with four other algorithms is used for image segmentation on two known images Lenna and Barbara. Efficiency comparison shows that the proposed algorithm has an appropriate and acceptable efficiency. The remainder of the paper is organized as follows: in sections 2 and 3, standard AFSA and k-means algorithm will be described respectively and in section 4, the proposed algorithm will be presented. Section 5 studies the experiments and analyzes their results and final section concludes the paper. In water world, fish can find areas that have more foods, which is done with individual or swarm search by fishes. According to this characteristic, artificial fish (AF) model is represented by prey, free-move, and swarm and follow behaviors. AFs search the problem space by those behaviors. The environment, which AF lives in, substantially is solution space and other AFs domain. Food consistence degree in water area is AFSA objective function. Finally, AFs reach to a point which its food consistence degree is maxima (global optimum). In artificial fish swarm algorithm, AF perceives external concepts with sense of sight. Current position of AF is shown by vector X=(x 1, x 2,à ¢Ã¢â€š ¬Ã‚ ¦, x n). The visual is equal to sight field of AF and Xv is a position in visual where the AF wants to go. Then if Xv has better food consistence than current position of AF, it goes one step toward X v which causes change in AF position from X to Xnext , but if the current position of AF is better than X v, it continues searching in its visual area. Food consistence in position X is fitness value of this position and is shown with f(X). The step is equal to maximum length of the movement. The distance between two AFs which are in Xi and Xj positions is shown by Dis ij =||X i-Xj|| (Euclidean distance). AF model consists of two parts of variables and functions. Variables include X (current AF position), step (maximum length step), visual (sight field), try-number (the maximum test interactions and tries) and crowd factor ÃŽÂ ´ (0 The standard k-means algorithm is summarized as follows: Initial position of K cluster centers is determined randomly. The following steps are repeated: a) for each data vector: data vector is allocated to a cluster that its Euclidean distance from its center is smaller than the other clusters centers. Distance from cluster center is calculated by Equation (1): (1) In Equation (1), Xp is data vector p, Zj is the center of cluster j and d is the number of dimensions of data vectors and cluster center vectors. b) After allocating all data to clusters, each of cluster centers is updated by Equation (2): (2) Where, nj is the number of data vectors that belong to cluster j and Cj is a subset of all data vectors which belong to cluster j. The resulted cluster center of Equation (2) is the average vector of data vectors comprising cluster. (a) and (b) steps are iterated until the stopping criterion is satisfied. In this section, the proposed algorithm is described. In the proposed algorithm, there exists a population of AFSAs AFs. This population of AFs is initialized randomly in problem space. Each AF consists of K cluster center positions in one dimensional image histogram space. Therefore, search space for AFSA for K cluster centers has K components. Fitness function which AFSA has to minimize is shown in Equation (3). (3) Clustering on histogram is done by Equation (3) based on color distribution between given images pixels. The image is divided into K clusters (Ci) according to color attribute by K-1 thresholds. In Equation (3), the distance between color Xj on image histogram and the center of a cluster which it belongs to ( Zi), is multiplied by the frequency of pixels (fj) which have color value Xj on given image. This value is computed for all color values with respect to the center of a cluster which they belong to. Each color becomes the member of a cluster in which their distance from that cluster center is less than other cluster centers. Finally, the obtained results of all clusters are summed with each other. Indeed, Equation (3) calculates sum of intra cluster distances for one dimensional gray scale images, which is one of the most well-known clustering criteria. For improving obtained results by AFSA, some modifications must do on its structure. The best found position by swarm members so far in AFSA is saved in bulletin and AF which has found it might go even toward worse positions with performing a free-move behavior. Therefore, AFs cannot utilize their best swarm experience for improving the convergence rate because they just save it in bulletin. On the other hand, performing free-move behavior is inevitable for maintaining diversity of the swarm. In this paper, to remove this problem, every AF except best AF can perform free-move behavior. In fact, during execution of the proposed algorithm, this behavior is not performed for the best AF of the swarm at all. Hence, the best found position by the swarm would be the position of the best AF of the swarm. As a result, other members of the swarm can move in the direction of the best found position by executing follow and swarm behaviors. The purpose of designing the proposed algorithm is to take advantages of both AFSA and k-means algorithms and remove their weaknesses. K-means is of high convergence rate, but its very sensitive to initializing the cluster centers and in the case of selecting inappropriate initial cluster centers, it could converge to a local optimum. AFSA can pass local optima to some extent but cannot guarantee reaching to global optima. However, AFSAs computational complexity for optimization process is much more than k-means. How the proposed algorithm functions remove weaknesses of these two algorithms and apply their advantages is as following: In the proposed algorithm, first, the AFs are initialized in AFSA. Each of AFSA contains K cluster centers (K-1 threshold) which are displaced in the problem space by performing AFSAs behaviors. AFSA continues to perform until the AFs converge. After convergence of AFSA, best AFs position including the best cluster centers which have found by AFs so far is considered as the input of k-means. Then, k-means algorithm starts working and while it is not converged, it continues working. Therefore, AFSA searches globally and as far as it can, it passes local optima. After convergence of AFSAs AFs, its output would have an appropriate initial cluster centers for k-means. Hence, after sending AFSAs outcome to k-means, this algorithm starts searching locally. Consequently, in the proposed algorithm, global search ability of AFSA has been used and after converging, a great part of optimization process will be given to k-means to utilize high capability of local search of this algorithm and its high convergence rate. Since initial cluster centers for k-means are obtained by AFSA and k-means is used for local search, k-means weakness of sensitivity to initial cluster centers is removed. But, AFSA capability may not be enough for preventing from being trapped in local optima. If this algorithm is trapped in local optima, it cannot present proper initial cluster values to k-means. Thereafter, according to low ability of k-means in passing local optima, the obtained result cannot be acceptable. To raise this problem, after convergence of AFSA, the output of this algorithm is sent to k-means. Simultaneously with starting of k-means, AFSAs AFs are initialized and start global search again. In fact, in one time of executing the proposed algorithm, AFSA has several times of chance to perform an acceptable global search. It should be noted that in the proposed algorithm, in each time of executing AFSA, AFs just search globally and converge after a short time and k-means undertakes the remaining of optimization process which is local search. Therefore, with respect to low computational complexity of k-means, huge amount of computations for local search is prevented. In the proposed algorithm, it has been tried to utilize this conserved computation load for giving new opportunities to AFSA in order to perform an acceptable global search in at least one of given opportunities to it. Hence, for each execution of global search by AFSA, k-means is also performed once. In the proposed algorithm, to determine the convergence of artificial fish swarm, the difference of obtained results in consecutive iterations of performing the algorithm is used. When particles converge, the obtained results difference in consecutive iterations decreases, so by considering a threshold for the difference between best AFs fitness values in iterations i and j, it can determine their convergence. In the proposed algorithm, because AFSA and k-means algorithms are performed multiple times , always, it has to save the best found cluster centers by algorithm so far. For this purpose, a blackboard is applied that each time k-means finishes after convergence of AFSA, the obtained result of that will be compared with saved result in blackboard. If obtained cluster centers are better than saved result in blackboard, saved value in blackboard is updated. K- means execution finishes when after two consecutive iterations of its execution, cluster centers wouldnt be displaced. Pseudo code of the proposed algorithm is represented in Figure (1). Experiments are done on two known gray scale images, Lenna and Barbara, of sizes 512*512 in Figure (2). In this paper, the well-known criterion of uniformity is used to compare images segmentation qualitatively [3] which is shown in Equation (4) (4) Where, c is the number of thresholds. Rj is the segmented region j. N is the total number of pixels in the given image, fi shows the gray level of pixel I,  µi is the mean gray level of pixels in jth region, finally, fmin and fmax are the minimum and maximum gray level of pixels in the given image, respectively. Usually, uà Ã‚ µ[0, 1] and larger amount for u declares that the thresholds are specified with better quality on the histogram. Proposed Algorithm: 1:for each AFi 2:initialize xi 3:Endfor 4:Blackboard = arg [min F(Xi)] 5:Repeat 6:for each AFi 7:Perform Swarm Behavior on Xi(t) and Compute Xi,swarm 8:Perform Follow Behavior on Xi(i) and Compute Xi,follow 9:if F(Xi,swarm) à ¢Ã¢â‚¬ °Ã‚ ¥ F(Xi,follow) 10:then Xi(t+1)= Xi,follow 11:Else 12:Xi(t+1)= Xi,swarm 13:Endif 14:Endfor 15:if swarm is converged 16:then Execute k-means on XBest-AF until stopping criterion of k-means is met 17:Endif 18:if F(Xk-means) à ¢Ã¢â‚¬ °Ã‚ ¤ F(Blackboard) 19:then Blackboard = Xk-means 20:reinitialize AFSA 21:Endif 22:until stopping criterion is met Figure (1): Pseudo code of proposed algorithm. The proposed algorithm along with standard AFSA, PSO algorithm, hybrid algorithm called KPSO [20], and k-means is used to segment two images, Lenna and Barbara. PSO and KPSO parameters are adjusted according to [6], and for k-means, initializing Forgy method is applied [21]. AFSA parameters and are adjusted according to [7]. AFSA settings in the proposed algorithm are the same as [7]. With respect to various experiments, if fitness value relating to Best AF is less than 0.1 in 3 iterations, it means that artificial fish swarm is converged. The following results are obtained from 50 times repeated experiments. Figure (3) shows segmented images, Lenna and Barbara, by the proposed algorithm with 5 and 3 thresholds. Figure 2: Orginal gray level Lenna (left) and Barbara (right) images Figure 3: The thresholded images of Lenna and Barbara using 5, and 2-level thresholds, from top to bottom. Average uniformity obtained from 5 algorithms on two images with thresholds 2, 3, 4 and 5 are shown in Table (1). As it is observed in Table (1), obtained results from the proposed algorithm is better than the other algorithms for all cases. AFSA algorithm has the worst result for all cases because of low ability in local search. K-means algorithm has found better results than AFSA because of high capability of k-means in local search. The reason for superiority of k-means to AFSA is the problem space property in histogram clustering. In fact, because of low dimensions of problem space in this environment, local search ability is of greater importance than global search ability. Also, it can reduce k-means weakness of sensitivity to initial values by means of one of the initializing methods of k-means like Forgy. Thereafter, with respect to considerable superiority of k-means local search ability in contrast to AFSA, k-means results are better than AFSAs. TABLE I: Comparison of uniformity for the five Algorithms Image T AFSA K-means PSO KPSO Proposed method Lenna 2 0.9138 0.9634 0.9730 0.9728 0.9775 3 0.9361 0.9749 0.9781 0.9783 0.9795 4 0.9495 0.9762 0.9816 0.9811 0.9826 5 0.9517 0.9804 0.9835 0.9834 0.9838 Barbara 2 0.9758 0.9761 0.9765 0.9768 0.9781 3 0.9783 0.9802 0.9808 0.9805 0.9820 4 0.9797 0.9834 0.9843 0.9851 0.9862 5 0.9822 0.9849 0.9855 0.9850 0.9884 Obtained results from PSO are better than k-means in all cases and its because of global search ability superiority of PSO to k-means. Moreover, in PSO, theres a trade-off between global search and local search abilities [16] and PSO also can perform a proper local search beside an acceptable global search. KPSO results are better than k-means results for all cases because after executing k-means in this algorithm, PSO algorithm is performed and improves obtained results from k-means. But obtained results from KPSO are not better than PSO for all cases. The reason is that sometimes k-means converges toward a local optimum and obtained result from that is not appropriate. Therefore, PSO is responsible for taking out the result from local optimum; however, it sometimes may not be successful. Indeed, improper result of k-means causes fast convergence of particles to local optimum. Obtained results from the proposed algorithm are better than other algorithms in all cases. The reason is u sage of strategies which have been used for global search in this algorithm. In fact, the proposed algorithm is successful in finding the global optima in most runs and can prevent final result from being trapped in local optima, whereas, this ability is observed less in other algorithms and they cannot guarantee passing local optima. This weakness causes that other algorithms to be of less robustness and not to be able to reach to almost the same results in their various implementations. Also, in the proposed algorithm, k-means algorithm performs local search after finding global optimum region by AFSA. Consequently, with respect to high ability of k-means in local search and taking proper initial cluster centers from AFSA, local search is done well in the proposed algorithm, too. As a result, both k-means and AFSA algorithms abilities are utilized in the proposed algorithm and the weakness of k- means algorithm cant decrease the algorithms efficiency. As it is observed in all algo rithms except KPSO, with rising up the number of thresholds, uniformity amount is improved. In KPSO, since the weakness of k-means has an undesirable effect on PSO efficiency, obtained results are not stable. In this paper, a new cooperative algorithm based on artificial fish swarm algorithm and k-means was proposed for image segmentation with respect to multi-level thresholding. In the proposed algorithm, AFSA performs global search and k-means is responsible for local search. The process of the proposed algorithm is such that the robustness and ability of preventing from being trapped in local optimums is improved. The proposed algorithm along with four other algorithms is used for segmenting 2 well-known images and obtained results are compared with each other. Experimental results show that segmented images quality by the proposed algorithm is much better than four other tested algorithms. [1] R. C. Gonzalez, and R. E. Woods, Digital image processing, In: Pearson Education India, Fifth Indian reprint, 2000. [2] S. Arora, J. Acharya, A. Verma., and K. Panigrahi, Multilevel thresholding for image segmentation through a fast statistical recursive algorithm, In: Journal on Pattern Recognition Letters 29, pp. 119125, 2008. [3] Maitra. M, A. Chatterjee, A hybrid cooperative-comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding, In: Journal on Expert System with applications 34, pp. 1341-1350, 2008. [4] M. Mignote, Segmentation by fusion of histogram-based k-means clusters in different color spaces, In: IEEE Transactions on Image Processing, 2008. [5] X. Yang, W. Zhao, Y. Chen, and X. Fang, Image segmentation with a fuzzy clustering algorithm based on Ant-Tree, In: Journal of Signal Processing 88, pp. 2453-2462, 2008. [6] Y. T. Kao, E. Zahara, and I. W. Kao, A hybridized approach to data clustering, In: Journal on Expert System with Applications 34, pp. 1754-1762, 2008. [7] D. Yazdani, S. Golyari, and M. R. Meybodi, A new hybrid approach for data clustering, In: 5th International Symposium on Telecommunication (IST) , pp. 932937, Tehran, 2010. [8] J. Kennedy, and R. C. Eberhart, Particle swarm optimization, In: IEEE International Conference on Neural Networks, 4, pp. 1942 1948, Perth, 1995. [9] A. A. A. Esmin, D. L. Pereira, and F. Araujo, Study of different approach to clustering data by using the particle swarm optimization algorithm, In: IEEE Congress on Evolutionary Computation, pp. 18171822, Hong Kong, 2008. [10] L. X. Li, Z. J. Shao, and J. X. Qian, An optimizing method based on autonomous animate: fish swarm algorithm, In: Proceeding of System Engineering Theory and Practice, pp. 32-38, 2002. [11] D. Yazdani, S. Golyari, and M. R. Meybodi, A new hybrid algorithm for optimization based on artificial fish swarm algorithm and cellular learning automata, In: 5th International Symposium on Telecommunication (IST), pp. 932-937, Tehran, 2010. [12] D. Yazdani, A. N. Toosi, and M. R. Meybodi, Fuzzy adaptive artificial fish swarm algorithm, In: 23 th Australian Conference on Artificial Intelligent, pp. 334-343, Adelaide, 2010. [13] J. Hu, X. Zeng, and J. Xiao, Artificial fish swarm algorithm for function optimization, In: International Conference on Information Engineering and Computer Science, pp. 1-4, 2010. [14] Y. Luo, W. Wei, and S. X. Wang, The optimization of PID controller parameters based on an improved artificial fish swarm algorithm, In: 3rd International Workshop on Advanced Computational Intelligence, pp. 328-332, 2010. [15] C. X. Li, Z. Ying, S. JunTao, and S. J. Qing, Method of image segmentation based on fuzzy c-means clustering algorithm and artificial fish swarm algorithm, In: International Conference on Intelligent Computing and Integrated Systems (ICISS) , pp. 254- 257, Guilin, 2010. [16] L. Xiao, A clustering algorithm based on artificial fish school, In: 2nd International Conference on Computer Engineering and Technology, pp. 766-769, 2010. [17] D. Bing, and D. Wen, Scheduling arrival aircrafts on multi- runway based on an improved artificial fish swarm algorithm, In: International Conference on Computational and Information Sciences, pp. 499-502, 2010. [18] J. A. Hartigan, An overview of clustering algorithms, In: New York: John Wiley Sons , 1975. [19] C. Y. Tsai, and I. W. Kao, Particle swarm optimization with selective particle regeneration for data clustering, In: Journal of Expert Systems with Applications 38, pp. 65656576, 2011. [20] D. W. der Merwe, and A. P. Engelbrecht, Data clustering using particle swarm optimization, In: Congress on Evolutionary Computation, pp. 215-220, 2003. [21] E. Forgy, Cluster analysis of multivariate data: efficiency vs. interpretability of classification, In: Biometrics 21, pp. 768, 1965

How the American Revolution Got Started

The events that took place before the American Revolution affected history in such a way giving the British and colonists the need to have a Revolution. The French and Indian war is the name for the war that took place between Great Britain and France in North America from 1754 to 1763. The aftermath of this war was a big part leading up to the American Revolution. The war changed economic, political, and social relations between the three European powers (Britain, France, and Spain) their colonies and colonists, and the natives that occupied the territories they demanded. The war finally ended with the signing of the treaty of Paris in 1763. France and Britain suffered financially because of the war. The stamp act came along in 1765, this was a direct tax imposed by the British parliament on the colonies. The act required that almost all printed materials must be produced on stamped paper. This consisted of legal documents, magazines, newspapers etc. The purpose of this tax was to pay for troops stationed in North America after the British Victory in the seven years’ war. The stamp Act congress was a meeting of representatives from the thirteen colonies. They discussed and acted upon the stamp act that was passed by the governing parliament of Great Britain, and did not include any representatives from the colonies. The congress then put together the declaration of the stamp act congress, which was fourteen points of colonial protest. They issued it to the king and parliament in hopes of repealing the stamp act. The Townsend acts were a series of laws passed beginning in 1767 by the Great Britain parliament in relation to the British colonies in North America, The acts being named after Charles Townshend who was the Chancellor of the Exchequer. Overall this was an internal tax on economic activity within a single colony; Townsend wanted the external taxes which was an economic activity that goes through a colony and into other parts of the country. Such as paint, glass, tea etc. He thought we should use the money to pay the colonial governor, other parliaments and the king’s salaries. Another event was the Boston massacre, an incident that happened in March of 1770. It started out as a street fight, the civilians being mad at the British for taxing everything and ended in Britain redcoats killing five civilians. This caused a lot rebellion in the British American colonies leading us towards the American Revolution. Five years later Shots were heard around the world. Paul Revere on April 18th yelled out the British regulars are coming! The first shot was fired by the British in Lexington, and then they went to Concord. Then our militia stopped them and turned them back to Boston. This was the start of the revolution, minute men were ready to stand in a minutes warning. The colonists were not going to stand for the British taking over their land and taxing them on all of their goods, so they fought for their rights. US constitution There were proposals at the philadelphia constitution convention in 1787. These proposals were the virginia plan, and the new jersey plan that people did not like. The US contitution was ratified after the Great compromise came into effect. Otherwise known as the conneticut plan. This consisted of a strong national government (tax, raising an army, regulated trade, and supremed laws). Another was the seperation of powers between legislative and executive. Also there would be two houses of congress, the senate and the House of Representatives. The states would be able to choose their US senators. Lastly there was the slavery 3/5th compromise meaning a slave counts as 3/5 of a person. When the U. S. Constitution was presented to the states, many people chose to be either Federalists or Anti-Federalists. Virginia and many other states were against the Constitution because there was no bill of rights included in it. James Madison was known as the â€Å"Father of the Constitution†, and he and Alexander Hamilton were two Federalists who supported the Constitution and explicated it in the Federalist papers (1788). On the other side George Mason, an Anti-Federalist, opposed the Constitution. Federalist (James madison) wanted a stronger government and argued to ratify the constitution. The US constitution will control factions which is a group of people with a common interest and economic seek to control government for own benefit. 1. ) Also the bigger the better in a national government, multiple factions will cancel one another out. 2. ) WE will choose the best among us to govern for the common good (republicanism) Anti Federalists – opposed to ratifying the constitution Partrick henry thought things were okay before the philadalphia convention and we were at peace. He also thought a large government would have to resort to tyranny to control everything menaing a loss of individual rights. He thought we should have lumped the states into a consolidated government. Samuel bryan thought governing over such a large area would be unable to address local concerns. Richard henry lee didn’t know it would be such a huge change.

The convention governing the International Whaling Commission (IWC)

President Clinton, when announcing his decision last October to delay the implementation of sanctions on Norway following that country's recommencement of commercial whaling, stated the United States' strong commitment to science- based international solutions to global conservation problems. The convention governing the International Whaling Commission (IWC) states similarly that its â€Å"regulations with respect to the conservation and utilization of whale resources †¦ shall be based on scientific findings†. But the practice differs greatly from the principle. The IWC took a decision in 1982 to impose a global moratorium on all commercial whaling at a time of growing scientific evidence that the Antarctic minke whale population, at least, could certainly sustain a limited harvest. Whaling countries, angered by this decision which they considered to be without scientific justification, hit back later in the 80's by making use of a provision in the IWC Convention which allowed them to issue permits to their nationals to catch some whales for the purpose of scientific research – research is conducted as a part of these â€Å"scientific† whaling operations, but is that their primary purpose? Most recently there is the proposal for a whale sanctuary throughout the Southern Ocean – a transparent attempt to prevent the resumption of whaling on the 3/4 million strong Antarctic minke population for reasons which have nothing to do with science. This has been accompanied by the unedifying spectacle of Western nations and â€Å"conservation† (or, more accurately, â€Å"preservationist†) groups desperately searching for some plausible surrogate scientific rationale with which to attempt to justify the proposal. These other reasons are discussed elsewhere in this volume. My brief is to address aspects of President Clinton's expressed concern at â€Å"the absence of a credible, agreed management and monitoring regime that would ensure that commercial whaling is kept within a science-based limit†. SUSTAINABLE UTILISATION Obviously such limits should be consistent with â€Å"sustainable utilisation† – but exactly what does that mean? The most ready analogy is that of a pensioner whose sole asset is a capital sum invested in a bank. Sustainable utilisation for him means living off the annual interest without dipping into the capital. In other words, harvesting only the natural annual growth of a population, without depleting it to a low level where this growth is greatly reduced. THE IWC'S NEW MANAGEMENT PROCEDURE In the 1970's, in response to mounting public criticism following the substantial depletion of many whale populations by whaling conducted under its aegis, the IWC introduced the so-called â€Å"New Management Procedure† (NMP). The underlying principles were fine – essentially to get whale populations to and keep them at reasonably high proportions of their size before exploitation started, by ensuring that catch limits set did not exceed sustainable levels. But the NMP proved unworkable in practice. Why? Not because there was anything wrong with the concept, but because the NMP didn't go far enough. It failed to specify how the â€Å"annual interest† (i.e. the sustainable catch level from a whale stock) was to be calculated, what data needed to be collected to do this, and how to take account of uncertainties. CALCULATING SUSTAINABLE YIELD LEVELS So how can sustainable yield levels be calculated? For the pensioner, the process is simple: to evaluate how much interest will become available annually, ask the bank teller how much capital is in his account and what the interest rate is, and then just multiply the two together. So why isn't fisheries management equally easy? – because the teller is unco- operative. All he will tell you, and only once a year, is how much you have in your account, which he can get wrong by typically 20%. And he certainly won't tell you directly what the interest rate is. How do we then get the information needed to be able to perform this key multiplication to calculate the sustainable yield for whale populations? For the capital component, sighting surveys are conducted from research vessels to determine the numbers of whales. By the standards normally attainable in fisheries research, the results obtained are good (error margins of typically 20%). The difficult component is the interest rate. Basically some (careful) exploitation is needed before this can be evaluated, because the calculation requires the information from a series of sighting surveys on how the size of the population changes in response to this harvesting. THE FUNDAMENTAL RISK-REWARD TRADE-OFF The bottom line then is that some trade-off is inevitable. If such initial harvests are kept too low, the potential productivity of the resource remains undiscovered. But if these catches are set too large, there is a high risk that unintended heavy depletion may occur before this is realised and corrective action can be taken. The goal of a risk-free harvesting strategy is unattainable, for exactly the same reason that no car or aircraft can ever be made completely â€Å"safe†. Risk can be reduced (though never eliminated), but only at the expense of higher costs – or correspondingly, lesser rewards in the form of smaller catches in resource utilisation terms. WHERE DOES THE COMPUTER COME IN? The role of the computer is to calculate the sizes of the anticipated trade-offs between risk and reward when harvesting whale populations. This is the basic function of the computer simulation trials used to test the IWC Scientific Committee's proposed â€Å"Revised Management Procedure† (RMP). Quantitative information about these trade-offs allows a sensible choice to be made between the extremes of rapid extinction of the resource under unsustainable catch levels, and complete protection which forbids any harvesting ever. WHAT IS THE DIFFERENCE BETWEEN A â€Å"MANAGEMENT PROCEDURE† AND THE TRADITIONAL APPROACH TO FISHERIES MANAGEMENT? How does such a â€Å"Management Procedure† approach differ from the usual methods used to regulate fisheries? There catch limits are calculated according to the current â€Å"best perceptions† of the status and productivity of the resource. But it is then not entirely clear how the answer obtained should be adjusted to take the inevitable uncertainties in these perceptions into account – in other words, how to make proper allowance for risk. In contrast, the â€Å"Management Procedure† approach puts such uncertainties up front, by insisting that if these current â€Å"best perceptions† are in error, the trend in catches set over the longer term must be such that the Procedure self-corrects before there is any substantial risk that the resource could be damaged. For example, it has been suggested that global climatic change could result in a change in the environment which is deleterious for whale stocks. The RMP has already been tested to ensure that catch limits for whales would be adjusted downwards appropriately should this occur. Why are such Procedures needed for whales in particular? Whales are long-lived animals and their populations can at best grow at only a few percent per annum. Thus even relatively low levels of catch, if continued, can lead to problems unless there is adequate monitoring and an option for adjusting catch limits. In other words, the risk involved in harvesting whale populations can be evaluated sensibly only for a Procedure which is to be consistently applied for a number of decades. Thus, as in sport, a Management Procedure involves all the parties concerned agreeing the rules before the game is played (and sticking to them during it!). IS THIS APPROACH BEING USED SUCCESSFULLY ELSEWHERE? This approach is not entirely new in fisheries. Iceland has been applying it in the management of its capelin fishery. Arising out of the IWC's initiative for whales, South Africa has now come to base catch limit decisions for its major fisheries for hake, sardine and anchovy on the approach. WHAT SORT OF CATCH REGIME FOR WHALES WOULD RESULT UNDER THE RMP? As far as catch limits for whales under the IWC Scientific Committee's proposed RMP are concerned, these would initially be set at annual levels of about 0.5% of current population sizes. That would apply to stocks of species not greatly depleted by past whaling activities, such as many of the world's minke whale populations. For stocks still markedly depleted such as the blue and fin whales of the Antarctic, this percentage would be considerably less – indeed zero for those and many other stocks for a number of decades yet. In addition, there would be provisions to ensure that catches are widely spread, rather than concentrated in a few small regions. This is necessary to provide safeguards against uncertainties in knowledge about the positions of the boundaries between stocks. The annual percentage take could be increased over time, but this would be permitted only provided the results from the monitoring population trends over time by sightings surveys suggest that such larger levels of catch are sustainable. However, if the survey series stops, catches are phased out quite rapidly. TO WHAT LEVEL OF RISK DOES THE RMP CORRESPOND? What risks would be involved in the application of the RMP to whale stocks? Broadly speaking, there would be no more than a 5% chance, even under the worst set of circumstances or misconceptions likely, that catches (other than perhaps ones of a negligible size) would be taken from a population reduced to more than 10% below its most productive level. (This is the so-called 54% â€Å"protection level† – an abundance 54% of that before any harvesting took place.) And populations would need to be reduced to well below that level before any real concerns about possible extinction might arise. HOW DOES THIS LEVEL OF RISK COMPARE TO THAT ACCEPTED IN HARVESTING OTHER OF THE WORLD'S MARINE RESOURCES? If this criterion (no more than a 5% chance that the population is below 54% of its pre-exploitation size for harvesting to be allowed) were applied to the rest of the world's fisheries, nearly all would have to be closed immediately. Off the northeast coast of the US and off western Europe, for example, harvesting continues from cod stocks which are below not just 50% of their pristine levels, but arguably less than as little as 10%. Even when allowing for biological differences between whales and fish, the low levels of risk some nations demand be met for harvesting the former, are totally inconsistent with the much higher levels which they are prepared to accept for exploiting their own stocks of the latter. ABORIGINAL WHALING ON THE BOWHEAD WHALE OFF ALASKA President Clinton's statement made reference to the aboriginal whaling on bowheads in which native Alaskans engage. Some years ago, there was justifiable concern that these activities were putting this population at risk. However, the US has commendably invested considerable research effort towards addressing this problem, with results which show that there can now be no serious scientific reservations that current levels of catch place the population under any real threat. Yet, were the RMP to be applied in this case, it is so risk averse that an immediate cessation of these whaling activities would be required. THE NMFS REVIEW OF THE RMP Recently, the US National Marine Fisheries Service commissioned an independent review of the RMP by a panel of seven North American scientists. Their brief to assimilate and comment upon seven years of work by the IWC Scientific Committee (without having had any prior involvement therein) in the short space of five days was a daunting one. The panel concluded that the RMP as it stood could be used safely for a period of at most 20 years, but also recommended that some further computer simulation trials be carried out. However, it seems to me that all the specific extra trials which they recommend have effectively already been carried out and considered by the IWC's Scientific Committee. It is unclear from the panel's written report whether they were unaware of this, or did actually have some reservations about what had been done, which their report fails to elaborate. Obviously the panel should clarify this ambiguity expeditiously to the IWC's Scientific Committee. NORWAY'S RESUMPTION OF COMMERCIAL WHALING Norway has, of course, resumed commercial whaling on minke whales. This it is legally entitled to do, since it lodged an objection to the IWC's 1982 moratorium decision. I understand that the annual catch limit set by the Norwegians for their overall operation is within the limit which the RMP would specify, so that there are no scientific grounds to query that decision. However, I understand also that the areal distribution of the catches permitted by Norway is not in accord with the provisions of the RMP, and I believe that legitimate questions can be directed at Norway on this point. Of course, such a deviation from the RMP does not necessarily mean that any real danger to the resource will eventuate. But if Norway does wish to depart from the RMP's provisions, I believe that it has some scientific obligation to present the results of computer simulation trials to the IWC's Scientific Committee to demonstrate that such deviations as they might plan do indeed not involve undue long term risk. THE POTENTIAL EFFECT OF INCREASED CONSUMPTION BY GROWING MARINE MAMMAL POPULATIONS ON COMMERCIAL FISH RESOURCES What of the concerns often expressed that increasing marine mammal populations will consume more fish and thus put fishing industries at risk? The counter argument often made is that there is no scientific proof that this is so. But equally, there is no scientific proof that it isn't. The scientific methods which have been used in the past to address this question have been crude, and there has been a justifiable argument that basing management decisions (such as a marine mammal cull, for example) upon their results would be premature. Marine science can never, by its nature, prove something without some residual doubt. But methods are being improved, and cases may soon arise where the preponderance of indications that growing numbers of marine mammals will impact fisheries is so strong, that hard decisions will have to be faced to avoid the chance that important industries are put at risk. For example, growing fur seal herds off southern Africa are now more than 2 million strong. Their consumption of commercial species equates to the total catch by all the fishing industries in the area, and their continued growth may constitute a threat to the region's most valuable fishery for hake. IN CONCLUSION To conclude, let me return to President Clinton's concern for science-based limits, and credible management and monitoring for potential commercial whaling. From the scientific side, the RMP has been more thoroughly researched and tested than any comparable marine resource management system worldwide. Its own requirement for regular sighting surveys, as well as the regular review process associated with its implementation for any species and region, ensures adequate monitoring. It is so risk averse that the only real scientific basis for questioning its immediate implementation is that it is so conservative that it will waste much of a potential harvest. If the United States fails to endorse the RMP, is there any way that the US could then avoid the judgement of complete hypocrisy, unless it immediately suspended not only the aboriginal whaling by Alaskans, but indeed closed every one of the country's fisheries?

The Between Britain And The Colonies - 974 Words

Many key events sharpened the divisions between Britain and the colonies in the late 1760s and early 1770s. The enforcement of new laws and tariffs helped in this division. These events brought the colonies together to eventually go against the British empire, becoming more aware of their desire for independence. In 1776, the London government decided to enforce new taxes on the Americans, these taxes were contrived by the cabinet s chief financial minister, Charles Townshend. Townshend swayed Parliament into creating new taxes on goods that were imported to the colonies such as glass, tea, paint, paper and lead. He also wanted to organize new commissioners for the board of customs that would collect these taxes and decrease the smuggling problem. Many people did not want or like the new enforcement procedures thus making leaders in several colonies reimpose the ban on importing British goods in 1768. 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Many colonists realizing their lack of representation in Parliament, which created political tension; British taxation of the colonies created economic tension; and citizens anger against both their lack