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论文类型：期刊论文

发表时间：2016-03-01

发表刊物：JOURNAL OF ELECTRONIC IMAGING

收录刊物：SCIE、EI、Scopus

卷号：25

期号：2

ISSN号：1017-9909

关键字：image retrieval; hue, saturation, and value color space; local Haar binary patterns; feature extraction; graph-based fusion

摘要：Recently, global and local features have demonstrated excellent performance in image retrieval. However, there are some problems in both of them: (1) Local features particularly describe the local textures or patterns. However, similar textures may confuse these local feature extraction methods and get irrelevant retrieval results. (2) Global features delineate overall feature distributions in images, and the retrieved results often appear alike but may be irrelevant. To address problems above, we propose a fusion framework through the combination of local and global features, and thus obtain higher retrieval precision for color image retrieval. Color local Haar binary patterns (CLHBP) and the bag-of-words (BoW) of local features are exploited to capture global and local information of images. The proposed fusion framework combines the ranking results of BoW and CLHBP through a graph-based fusion method. The average retrieval precision of the proposed fusion framework is 83.6% on the Corel-1000 database, and its average precision is 9.9% and 6.4% higher than BoW and CLHBP, respectively. Extensive experiments on different databases validate the feasibility of the proposed framework. (C) 2016 SPIE and IS&T