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标题：IAPSO-TCI: Improved Ant and Particle Swarm based Optimization Techniques for Classifying Imagery
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作者：Rebika Rai ; Ratika Pradhan ; M.K.Ghose 等
期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2014
卷号：5
期号：4
页码：5963-5969
出版社：TechScience Publications
摘要：The biologically inspired world comprising of social insect metaphor for solving out wide range of dilemma has become potentially promising area in most recent duration focusing on indirect or direct coordination’s among diverse artificial agents. Swarm [8] apparently is a disorganized collection / population of moving individual that tends to cluster together while each individual seems to be moving in random directions. Swarm Intelligence techniques include Particle swarm optimization, Ant Code Optimization, Biogeography based optimization, Bee Colony Optimization, Stochastic Diffusion Search, Bacterial foraging optimization. Classification is the computational procedure [1] [3] that arrange the images into groups according to their similarities. Plentiful methods for classification have been designed and investigating novel means to increase classification exactness has been a key topic. Ant Colony Optimization (ACO) [6] [11] [18] is an algorithm motivated by the foraging behaviour of ants wherein ants leaves the volatile substance called pheromone on the soil surface for the purpose of collective contact via indirect communications. Particle Swarm Optimization is an approach to problems whose solutions can be represented as a point in an n-dimensional solution space wherein number of particles [13] [19] are randomly set into motion through this space. In each of the iteration, they observe the "fitness" of themselves and their neighbours and "emulate" successful neighbours by moving towards them. This paper focuses on improved Methodology of Swarm Computing for classifying imagery termed as IAPSO-TCI exploring Improved Ant and Particle Swarm based Optimization using a traditional classifier SVM (Support Vector Machines) for edge detection and image classification
关键词：Classification; Imagery; Feature Extraction;Feature Selection; Pheromone; Swarm; Ant Colony;Optimization; Particle Swarm Optimization; Support Vector;Machines; Edge Detection; Image Classification