Batik Image Retrieval Using ODBTC Feature and Particle Swarm Optimization

Abstract

This paper proposes an effective and efficient approach to Batik image retrieval using Ordered Dither Block Truncation Coding (ODBTC) feature. Similarity degree between two images can be easily investigated under similarity distance score between their feature descriptors. As documented in the experimental section, the feature descriptor outperforms the former existing schemes under Batik image database. The Particle Swarm Optimization (PSO) iteratively searches the optimal similarity weighting constants to further improve the image retrieval performance. Thus, a set of retrieved images become more satisfactory and acceptable for user desire and preference.