Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Gene Clustering Using Self-Organizing Maps and Particle Swarm Optimization
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Hybridization of the ant colony optimization with the k-means algorithm for clustering
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
IEEE Transactions on Circuits and Systems for Video Technology
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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In this paper the problem of the image classification based on biologically inspired optimization systems is addressed. Recent developments in applied and heuristic optimization have been strongly influenced and inspired by natural and biological system. The findings of recent studies are showing strong evidence to the fact that some aspects of the collaborative behavior of social animals such as ants and birds can be applied to solve specific problems in science and engineering. Two algorithms based on this paradigm Ant Colony Optimization and Particle Swarm Optimization are investigated in this paper. The comparative evaluation of the recently developed techniques by the authors for optimizing the COP-K-means and the Self Organizing Feature Maps for the application of Binary Image Classification is presented. The precision and retrieval results are used as the metrics of comparison for both classifiers.