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ISRN Artificial Intelligence
Volume 2012 (2012), Article ID 426957, 10 pages
http://dx.doi.org/10.5402/2012/426957
Research Article

Application of Artificial Bee Colony Optimization Algorithm for Image Classification Using Color and Texture Feature Similarity Fusion

1Department of Computer Science and Engineering, Kumaraguru College of Technology, Tamil Nadu, Coimbatore 641049, India
2Department of Electrical and Electronics Engineering, PSG College of Technology, Tamil Nadu, Coimbatore 641004, India

Received 9 September 2011; Accepted 24 October 2011

Academic Editor: C. Chen

Copyright © 2012 D. Chandrakala and S. Sumathi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

With the advancement in image capturing device, the image data is being generated in high volumes. The challenging and important problem in image mining is to reveal useful information by grouping the images into meaningful categories. Image retrieval is extensively required in recent decades because CBIR is regarded as one of the most effective ways of accessing visual data. Conventionally, the way of searching the collections of digital image database is by matching keywords with image caption, descriptions and labels. Keyword based searching method provides very high computational complexity and user has to remember the exact keywords used in the image database. Instead, our paper proposes image retrieval system with Artificial Bee Colony optimization algorithm by fusing similarity score based on color and texture features of an image thereby achieving very high classification accuracy and minimum retrieval time. In this scheme, the color is described by color histogram method in HSV space and texture represented by contrast, energy, entropy, correlation and local stationary over the region in an image. The proposed Comprehensive Image Retrieval scheme fuses the color and texture feature based similarity score between query and all the database images. The experimental results show that the proposed method is superior to keywords based retrieval and content based retrieval schemes with individual low-level features of image.