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论文类型：期刊论文

发表时间：2015-11-01

发表刊物：JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION

收录刊物：SCIE、EI、Scopus

卷号：33

页面范围：104-114

ISSN号：1047-3203

关键字：Image retrieval; HSV color space; Texton detection; Global Correlation Descriptor (GCD); Global Correlation Vector (GCV); Global Correlation Vector (DGCV); Feature fusion; Structure element correlation (SEC)

摘要：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. (C) 2015 Elsevier Inc. All rights reserved.