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Fusion framework for color image retrieval based on bag-of-words model and color local Haar binary patterns

[+] Author Affiliations
Li Li, Laihang Yu

Dalian University of Technology, School of Computer Science and Technology, Faculty of Electronic Information and Electrical Engineering, No. 2 Linggong Road, Ganjingzi District, Dalian 116024, China

Lin Feng

Dalian University of Technology, School of Computer Science and Technology, Faculty of Electronic Information and Electrical Engineering, No. 2 Linggong Road, Ganjingzi District, Dalian 116024, China

Dalian University of Technology, School of Innovation and Entrepreneurship, No. 2 Linggong Road, Ganjingzi District, Dalian 116024, China

Jun Wu

Dalian University of Technology, School of Innovation and Entrepreneurship, No. 2 Linggong Road, Ganjingzi District, Dalian 116024, China

Shenglan Liu

Dalian University of Technology, School of Control Science and Engineering, Faculty of Electronic Information and Electrical Engineering, No. 2 Linggong Road, Ganjingzi District, Dalian 116024, China

J. Electron. Imaging. 25(2), 023022 (Apr 18, 2016). doi:10.1117/1.JEI.25.2.023022
History: Received November 16, 2015; Accepted March 7, 2016
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Abstract.  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.

© 2016 SPIE and IS&T

Citation

Li Li ; Lin Feng ; Laihang Yu ; Jun Wu and Shenglan Liu
"Fusion framework for color image retrieval based on bag-of-words model and color local Haar binary patterns", J. Electron. Imaging. 25(2), 023022 (Apr 18, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.2.023022


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