Journal
PATTERN RECOGNITION
Volume 48, Issue 8, Pages 2554-2566Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2015.02.005
Keywords
Image retrieval; Gray level co-occurrence matrix; Visual attention; Saliency structure model; Saliency structure histogram
Funding
- National Natural Science Foundation of China [61233011, 61202272, 61463008, 61202318, 61363035]
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It is a very challenging problem to Well simulate visual attention mechanisms for content-based image retrieval. In this paper, we propose a novel computational visual attention model, namely saliency structure model, for content-based image retrieval. First, a novel visual cue, namely color volume, with edge information together is introduced to detect saliency regions instead of using the primary visual features (e.g., color, intensity and orientation). Second, the energy feature of the gray-level co-occurrence matrices is used for globally suppressing maps, instead of the local maxima normalization operator in Itti's model. Third, a novel image representation method, namely saliency structure histogram, is proposed to stimulate orientation-selective mechanism for image representation within CBIR framework. We have evaluated the performances of the proposed algorithm on two datasets. The experimental results clearly demonstrate that the proposed algorithm significantly outperforms the standard BOW baseline and micro-structure descriptor. (C) 2015 Elsevier Ltd. All rights reserved.
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