Bioinspired metaheuristics for image segmentation

Valentín Osuna-Enciso

摘要

v:* {behavior:url(#default#VML);} o:* {behavior:url(#default#VML);} w:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} PhD thesis defended on 2nd December, 2013 . In general, the purpose of Global Optimization (GO) is to find the global optimum of an objective function defined inside a search space, and it has applications in many areas of science, engineering, economics, among other, where mathematical modeling is used. GO algorithms are divided into two groups: deterministic and evolutionary. Since deterministic methods only provide a theoretical guarantee of locating local minimums of the objective function, they often face great difficulties in solving GO problems. On the other hand, evolutionary methods are usually faster in locating a global optimum than deterministic ones, because they operate on a population of candidate solutions, so they have a bigger likelihood of finding the global optimum, and even they have a better adaptation to black box formulations or complicated function’ forms. Even though during the last decade has had an important increasing in the area of metaheuristics applied to optimization, still is considered the searching of such methods as an open problem in research, due mainly to the fact that they still present difficulties, such as premature convergence and difficulty to overcome local optimums. Therefore, in this work it is proposed a bio inspired algorithm, who takes as inspiration the mechanism of allostasis. Allostasis is a biological term which explains how the modifications of specialized organ conditions inside the body allow achieving stability when an unbalance health condition is presented. If a body decompensation happens, according to the allostatic mechanisms, several body conditions compound by blood pressure, oxygen tension and others indexes are proved in order to get a stability state in health. By using the allostatic mechanisms as a metaphor, it is that we propose a metaheuristic algorithm, which we called Allostatic Optimization (AO). Such algorithm provides a searching procedure that is population-based, under which all the individuals, seen as body conditions, are defined in a multidimensional search space; aforementioned agents are either generated or modified by mean of several evolutionary operators who emulate the various operations used by the allostatic process, whereas an objective function evaluates the individual's capacity (body condition) to reach a steady health state (good solution). AO is compared against DE, ABC and PSO and, different to them, the proposed algorithm favors the exploration process and eliminates some flaws related with premature convergence. By making such a comparison, it was found that in 57% of the functions the diversity maintained by AO helps the convergence of the algorithm, due to the fact that introduces operators that avoid particle concentration on some regions of the search space, favoring exploration. It was also found that maintaining a high diversity in the population does not guarantee the proper convergence of AO in all the benchmark functions, so a possible future work in this part of the investigation could be a more complete study of the relations among properties of functions, diversity and their relations with adequate convergence of the algorithm. AO was also used in image segmentation by using a mixture of functions; with the purpose of demonstrate the utility of the algorithm in a particular family of problems. One way to achieve segmentation is by mean of threshold selection, where each pixel that belongs to a determined class is labeled according to the selected threshold, giving as a result pixel groups that share visual characteristics in the image. In this work we use a method based of a mixture of Cauchy functions to approximate 1D histograms of gray level images, and it was found that AO improves the segmentation quality in about 14% when it was compared with Otsu’s method over known image benchmarks. Moreover, the metaheuristic algorithms DE, ABC and PSO were compared when they were applied to image segmentation by using a method that uses a mixture of Gaussian functions to approximate 1D histograms, because an analysis of such kind was not found in literature; the empirical results were that DE gives the best results in terms of convergence speed as well as quality of segmentation when compared against ground-truth images. Normal 0 21 false false false ES-MX X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";}

机译：v ：* {behavior：url（＃default＃VML）;} o ：* {behavior：url（＃default＃VML）;} w ：* {behavior：url（＃default＃VML）;} .shape {behavior：url（＃default＃VML）;}博士学位论文于2013年12月2日辩护。通常，全局优化（GO）的目的是找到在搜索空间内定义的目标函数的全局最优值，它在科学，工程，经济学等许多领域都得到了应用，其中使用了数学建模。 GO算法分为两类：确定性算法和进化算法。由于确定性方法仅能为定位目标函数的局部最小值提供理论上的保证，因此它们在解决GO问题时常常面临很大的困难。另一方面，进化方法通常在确定全局最优值方面比确定性方法快，因为它们在大量候选解决方案上运行，因此它们更有可能找到全局最优值，甚至对黑色的适应性也更好。盒式或复杂函数的形式。尽管在最近十年中，用于优化的元启发式方法领域有了重要的增长，但仍被认为是对此类方法的研究，作为研究中的一个开放问题，这主要是因为它们仍然存在困难，例如过早收敛。和克服局部最优的困难。因此，在这项工作中，提出了一种生物启发算法，该算法以同种异体代谢机制为灵感。同种异体是一个生物学术语，它解释了当出现不平衡的健康状况时，体内特殊器官状况的改变如何实现稳定。如果发生身体代偿失调，则根据同种异体调节机制，证明几种身体状况会因血压，氧气张力和其他指标而复合，从而获得健康的稳定状态。通过使用静力学机制作为隐喻，我们提出了一种元启发式算法，我们称其为“ Allostatic Optimization（AO）”。这种算法提供了一种基于人群的搜索程序，在该程序中，所有被视为身体状况的个体都被定义在多维搜索空间中。可以通过模拟进化过程中使用的各种操作的几个进化算子来生成或修改上述代理，而目标函数则评估个体达到稳定健康状态的能力（身体状况）（良好的解决方案）。将AO与DE，ABC和PSO进行比较，并且与它们不同，该算法有利于探索过程，并消除了与过早收敛有关的一些缺陷。通过进行这样的比较，发现在AO的57％函数中，多样性的引入有助于算法的收敛，原因是引入了避免在搜索空间的某些区域集中粒子的运算符，从而有利于探索。还发现维持种群的高度多样性并不能保证在所有基准功能中AO的适当收敛，因此在此部分调查中可能的未来工作可能是更全面地研究功能属性之间的关系，多样性以及它们之间的关系以及算法的充分收敛性。 AO还通过混合使用来进行图像分割。目的是演示该算法在特定问题系列中的实用性。一种实现分割的方法是通过阈值选择，其中根据所选阈值标记属于已确定类别的每个像素，从而得到共享图像中视觉特征的像素组。在这项工作中，我们使用了一种基于柯西函数混合的方法来近似灰度图像的一维直方图，并且发现在已知的图像基准上，与大津的方法相比，AO将分割质量提高了约14％。此外，将元启发式算法DE，ABC和PSO在使用高斯函数混合逼近一维直方图的方法进行图像分割时进行了比较，因为在文献中未发现这种分析。实验结果表明，与地面真实图像相比，DE在收敛速度和分割质量方面提供了最佳结果。正常0 21否否否ES-MX X-NONE X-NONE / *样式定义* / table.MsoNormalTable {mso-style-name：“ Tabla normal”; mso-tstyle-rowband-size：0; mso-tstyle-colband-size：0; mso-style-noshow：是; mso-style-priority：99; mso-style-parent：“”; mso-padding-alt：0cm 5.4pt 0cm 5.4pt; mso-para-margin：0cm; mso-para-margin-bottom：.0001pt; mso分页：寡妇孤儿;字体大小：10.0pt;字体家族：“ Times New Roman”，“ serif”;}

Bioinspired metaheuristics for image segmentation

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