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论文类型：期刊论文

发表时间：2015-03-25

发表刊物：NEUROCOMPUTING

收录刊物：SCIE、EI

卷号：152

页面范围：359-368

ISSN号：0925-2312

关键字：Saliency detection; Background; Foreground; Seed; Integration; Unification

摘要：In this paper, we propose a bottom-up visual saliency detection algorithm. Different from most previous methods that mainly concentrate on image object, we take both background and foreground into consideration. First, we collect background seeds from image border superpixels by boundary information and calculate a background-based saliency map. Second, we select foreground seeds by segmenting the first-stage saliency map via adaptive threshold and compute a foreground-based saliency map. Third, the two saliency maps are integrated by the proposed unified function. Finally, we refine the integrated result to obtain a more smooth and accurate saliency map. Moreover, the unified formula also proves to be effective in combining the proposed approach with other models. Experiments on publicly available data sets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods. (C) 2014 Elsevier B.V. All rights reserved.