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

Li Li; Lin Feng; Laihang Yu; Jun Wu; Shenglan Liu

doi:10.1117/1.JEI.25.2.023022

18 April 2016 Fusion framework for color image retrieval based on bag-of-words model and color local Haar binary patterns

Li Li, Lin Feng, Laihang Yu, Jun Wu, Shenglan Liu

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 2, 023022 (April 2016). https://doi.org/10.1117/1.JEI.25.2.023022

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.

Citation Download 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," Journal of Electronic Imaging 25(2), 023022 (18 April 2016). https://doi.org/10.1117/1.JEI.25.2.023022

Published: 18 April 2016

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