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Performance Enhanced Optimization based Image Retrieval System

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Evolutionary Computation for Optimization Techniques
© 2010 by IJCA Journal
Number 1 - Article 6
Year of Publication: 2010
Authors:
Tessy Annie Varghese
10.5120/1529-132

Tessy Annie Varghese. Performance Enhanced Optimization based Image Retrieval System. IJCA Special Issue on Evolutionary Computation (1):31–34, 2010. Full text available. BibTeX

@article{key:article,
	author = {Tessy Annie Varghese},
	title = {Performance Enhanced Optimization based Image Retrieval System},
	journal = {IJCA Special Issue on Evolutionary Computation},
	year = {2010},
	number = {1},
	pages = {31--34},
	note = {Full text available}
}

Abstract

Image retrieval is a system for browsing, searching the query image and retrieving similar images from large databases. A wide variety of features can be used for image retrieval. This process selects a subset of relevant features from a group of features of the image. It also helps to acquire better understanding about the image by describing which the important features are. The accuracy can be improved by increasing the number of features selected. But this increases the complexity of the retrieval system. The performance can be improved by removing the irrelevant and redundant features from taking into consideration. This is known as optimization. Many optimization techniques can be used. Ant Colony Optimization (ACO) is the technique proposed in this paper. With ACO, the image features are selected and images are retrieved from databases with high accuracy.

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