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发布时间：2019-03-13

论文类型：期刊论文

发表时间：2016-01-15

发表刊物：NEUROCOMPUTING

收录刊物：Scopus、EI、SCIE

卷号：173

期号：,SI

页面范围：406-417

ISSN号：0925-2312

关键字：Saliency detection; Generative model; Discriminative model; Sparse coding

摘要：In this paper, we propose a visual saliency detection algorithm which incorporates both generative and discriminative saliency models into a unified framework. First, we develop a generative model by defining image saliency as the sparse coding residual based on a learned background dictionary. Second, we introduce a discriminative model by solving an optimization problem that exploits the intrinsic relevance of similar regions for regressing region-based saliency to the smooth state. Third, a weighted sum of multi-scale region-level saliency is computed as the pixel-level saliency, which generates a more continuous and smooth result. Furthermore, object location is also utilized to suppress background noise, which acts as a vital prior for saliency detection. Experimental results show that the proposed algorithm generates more accurate saliency maps and performs favorably against the state-of-the-art saliency detection methods on three publicly available datasets. (C) 2015 Elsevier B.V. All rights reserved.