The image descriptors based on multi-features fusion have better performance than that based on simple feature in content-based image retrieval (CBIR). However, these methods still have some limitations: 1) the methods that define directly texture in color space put more emphasis on color than texture feature; 2) traditional descriptors based on histogram statistics disregard the spatial correlation between structure elements; 3) the descriptors based on structure element correlation (SEC) disregard the occurring probability of structure elements. To solve these problems, we propose a novel image descriptor, called Global Correlation Descriptor (GCD), to extract color and texture feature respectively so that these features have the same effect in CBIR. In addition, we propose Global Correlation Vector (GCV) and Directional Global Correlation Vector (DGCV) which can integrate the advantages of histogram statistics and SEC to characterize color and texture features respectively. Experimental results demonstrate that GCD is more robust and discriminative than other image descriptors in CBIR
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