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论文类型：期刊论文

发表时间：2018-02-01

发表刊物：JOURNAL OF CENTRAL SOUTH UNIVERSITY

收录刊物：SCIE、EI

卷号：25

期号：2

页面范围：259-276

ISSN号：2095-2899

关键字：local binary patterns; hue; saturation; value (HSV) color space; graph fusion; image retrieval

摘要：Two lines of image representation based on multiple features fusion demonstrate excellent performance in image retrieval. However, there are some problems in both of them: 1) the methods defining directly texture in color space put more emphasis on color than texture feature; 2) the methods extract several features respectively and combine them into a vector, in which bad features may lead to worse performance after combining directly good and bad features. To address the problems above, a novel hybrid framework for color image retrieval through combination of local and global features achieves higher retrieval precision. The bag-of-visual words (BoW) models and color intensity-based local difference patterns (CILDP) are exploited to capture local and global features of an image. The proposed fusion framework combines the ranking results of BoW and CILDP through graph-based density method. The performance of our proposed framework in terms of average precision on Corel-1K database is 86.26%, and it improves the average precision by approximately 6.68% and 12.53% over CILDP and BoW, respectively. Extensive experiments on different databases demonstrate the effectiveness of the proposed framework for image retrieval.