Ant Based Swarm Computing for Image Classification - A Brief Survey
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10.5120/9413-1004 |
Rebika Rai, Ratika Pradhan and M K Ghose. Article: Ant Based Swarm Computing for Image Classification - A Brief Survey. IJCA Special Issue on Computational Intelligence & Information Security CIIS(1):17-21, November 2012. Full text available. BibTeX
@article{key:article,
author = {Rebika Rai and Ratika Pradhan and M. K. Ghose},
title = {Article: Ant Based Swarm Computing for Image Classification - A Brief Survey},
journal = {IJCA Special Issue on Computational Intelligence & Information Security},
year = {2012},
volume = {CIIS},
number = {1},
pages = {17-21},
month = {November},
note = {Full text available}
}
Abstract
The social insect metaphor for solving problems has become an emerging topic in the recent years. This approach emphasizes on direct or indirect interactions among simple agents. Swarm Intelligence is the collective behavior of decentralized [8], self-organized [4] system whereby the collective behavior of agent interacting locally with the environment causes coherent global pattern to emerge. Classification is a computational procedure that sorts the image into groups according to their similarities [5]. Images can be similar but to measure the similarity pixel-to-pixel comparison is made. Numerous methods for classification have been developed. Exploring new methods to increase classification accuracies have been a key topic. This paper explores the swarm computing methods called Ant Colony Optimization (ACO) to classify imagery.
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