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论文类型：期刊论文

发表时间：2015-10-01

发表刊物：PATTERN RECOGNITION

收录刊物：SCIE、EI、Scopus

卷号：48

期号：10

页面范围：3258-3267

ISSN号：0031-3203

关键字：Visual saliency; Locality-constrained linear coding; Global and local cues

摘要：Previous saliency detection algorithms used to focus on low level features directly or utilize a bunch of sample images and manually labeled ground truth to train a high level learning model. In this paper, we propose a novel coding-based saliency measure by exploring both global and local cues for saliency computation. Firstly, we construct a bottom-up saliency map by considering global contrast information via low level features. Secondly, by using a locality-constrained linear coding algorithm, a top-down saliency map is formulated based on the reconstruction error. To better exploit the local and global information, we integrate the bottom-up and top-down maps as the final saliency map. Extensive experimental results on three large benchmark datasets demonstrate that the proposed approach outperforms 22 state-of-the-art methods in terms of three popular evaluation measures, i.e., the Precision and Recall curve, Area Under ROC Curve and F-measure value. Furthermore, the proposed coding-based method can be easily applied in other methods for significant improvement. (C) 2014 Elsevier Ltd. All rights reserved.