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Journal of Electronic Imaging

Fusion framework for color image retrieval based on bag-of-words model and color local Haar binary patterns
Author(s): Li Li; Lin Feng; Laihang Yu; Jun Wu; Shenglan Liu
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Paper 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.

Paper Details

Date Published: 18 April 2016
PDF: 17 pages
J. Electron. Imag. 25(2) 023022 doi: 10.1117/1.JEI.25.2.023022
Published in: Journal of Electronic Imaging Volume 25, Issue 2
Show Author Affiliations
Li Li, Dalian Univ. of Technology (China)
Lin Feng, Dalian Univ. of Technology (China)
Laihang Yu, Dalian Univ. of Technology (China)
Jun Wu, Dalian Univ. of Technology (China)
Shenglan Liu, Dalian Univ. of Technology (China)

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