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《Journal of Graphics》 2017-04
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Image Retrieval Based on Improved Color Histogram and Gray Level Co-occurrence Matrix

WU Qingtao;CAO Zaihui;SHI Jinfa;Zhengzhou University of Aeronautics;Collaborative Innovation Center for Aviation Economy Development;  
There are the problems that the extracted color feature is high dimension based on the traditional color histogram, and the direction of texture is neglected based on the traditional co-occurrence matrix. A new image retrieval algorithm combining the improved color histogram and gray level co-occurrence matrix algorithm is proposed. The K-means clustering is used to cluster the detected images in order to reduce the number of colors. The image codes are computed to form the color histogram based on vector codes in the HSV space. So the color features are extracted. The gray level co-occurrence matrix is used to extract the four eigenvalues of the detected image, and the four eigenvalues are combined with the weighting factor determined by the direction measure. The texture eigenvectors are obtained from normalizing the fused components. Finally, weighted average is used to fuse the feature distance of color and texture. Compared with the other two algorithms, experimental results show that our algorithm has higher recall and precision in general images and textured images.
【Key Words】: image retrieval color histogram color clustering vector coding gray level co-occurrence matrix direction measurement
【Fund】: 国家自然科学基金项目(71371172);; 河南省高等学校重点科研项目(15A520105);; 2017年度河南省科技攻关项目(172102210523)
【CateGory Index】: TP391.41
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