Research Article
Image classification using biologically inspired systems
@INPROCEEDINGS{10.1145/1374296.1374326, author={Tomas Piatrik and Krishna Chandramouli and Ebroul Izquierdo}, title={Image classification using biologically inspired systems}, proceedings={2nd International ICST Conference on Mobile Multimedia Communications}, publisher={ACM}, proceedings_a={MOBIMEDIA}, year={2006}, month={9}, keywords={Binary Image Cassifier; COP-K-Means; Self Organizing Feature Maps (SOFM); Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO).}, doi={10.1145/1374296.1374326} }- Tomas Piatrik
Krishna Chandramouli
Ebroul Izquierdo
Year: 2006
Image classification using biologically inspired systems
MOBIMEDIA
ACM
DOI: 10.1145/1374296.1374326
Abstract
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.