Image Retrieval Using Modified Color Variation Co-occurrence Matrix

Authors:
Yung-Fu Chen;Yung-Kuan Chan;Guo-Uei Chang;Meng-Chi Tsao;Yu-Jhuang Syu;Chuen-Horng Lin
Affiliations:
Department of Health Services Administration, China Medical University, Taichung, Taiwan 404;Department of Management of Information Systems, National Chung Hsing University, Taichung, Taiwan 402;Department of Information Management, National Taichung Institute of Technology, Taichung, Taiwan 404;Department of Information Management, National Taichung Institute of Technology, Taichung, Taiwan 404;Institute of Computer Science and Information Technology, National Taichung Institute of Technology, Taichung, Taiwan 404;Department of Information Management, National Taichung Institute of Technology, Taichung, Taiwan 404 and Institute of Computer Science and Information Technology, National Taichung Institute of T ...
Venue:
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Year:
2008

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Abstract

Texture is widely used as an important feature for content based image retrieval (CBIR). In this paper, color variation co-occurrence matrix (CVCM) modified from a previous investigation has been proposed for describing texture characteristics of an image. An image is processed and converted into four CVCMs based on overlapping 3 x 3 windows scanning from top to bottom and left to right. Each 3 x 3 window is divided into 4 grids with the central pixel located at four different corners. First, the original image with a size of Nxx Nyis converted into 4 motif matrices from individual scanning windows according to the traversal between the differences of four adjacent pixels. By contrasting to a previous method that 6 motif patterns were discriminated, an additional pattern was used to resolve ambiguity amounting to 7 motif patterns in total. By computing the probability of adjacent pattern pairs, an image retrieval system has been developed. The performance of the CVCM system was evaluated in the experiments using four different image sets. The experimental results reveal that the proposed CVCM-based image retrieval system outperforms the methods proposed by Huang and Dai and Jhanwar et al.