An ant colony optimization algorithm based image classification method for content-based image retrieval in cloud computing environment

Abstract

Feature selection and feature extraction are the most important steps in classification problems. Feature selection is commonly used to reduce dimensionality of datasets with tens or hundreds of thousands of features which would be impossible to process further. One of the problems in which feature selection is essential is content-based image classification problems. This paper presents a novel method for image classification method based on an ant colony optimization algorithm which can significantly improve the classification performance for content-based image retrieval in cloud computing environment. Ant colony optimization algorithm is inspired by observation on real ants in their search for the shortest paths to food sources. The proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. We compared the previous algorithm with the proposed algorithm in terms of image classification performance. As a result, the proposed algorithm showed higher performance in terms of accuracy. © 2012 Springer-Verlag Berlin Heidelberg.