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论文类型：期刊论文

发表时间：2013-07-01

发表刊物：IEEE SIGNAL PROCESSING LETTERS

收录刊物：SCIE、EI、Scopus

卷号：20

期号：7

页面范围：637-640

ISSN号：1070-9908

关键字：Center prior; contrast prior; salient object detection; smoothness prior

摘要：Object level saliency detection is useful for many content-based computer vision tasks. In this letter, we present a novel bottom-up salient object detection approach by exploiting contrast, center and smoothness priors. First, we compute an initial saliency map using contrast and center priors. Unlike most existing center prior based methods, we apply the convex hull of interest points to estimate the center of the salient object rather than directly use the image center. This strategy makes the saliency result more robust to the location of objects. Second, we refine the initial saliency map through minimizing a continuous pairwise saliency energy function with graph regularization which encourages adjacent pixels or segments to take the similar saliency value (i.e., smoothness prior). The smoothness prior enables the proposed method to uniformly highlight the salient object and simultaneously suppress the background effectively. Extensive experiments on a large dataset demonstrate that the proposed method performs favorably against the state-of-the-art methods in terms of accuracy and efficiency.