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论文类型：期刊论文
发表时间：2016-09-01
发表刊物：VISUAL COMPUTER
收录刊物：SCIE、EI、Scopus
卷号：32
期号：9
页面范围：1121-1132
ISSN号：0178-2789
关键字：Mesh saliency; Absorbing Markov chain; Feature space; Foreground cues
摘要：We propose a mesh saliency detection approach using absorbing Markov chain. Unlike most of the existing methods based on some center-surround operator, our method employs feature variance to obtain insignificant regions and considers both background and foreground cues. Firstly, we partition an input mesh into a set of segments using Ncuts algorithm and then each segment is over segmented into patches based on Zernike coefficients. Afterwards, some background patches are selected by computing feature variance within the segments. Secondly, the absorbed time of each node is calculated via absorbing Markov chain with the background patches as absorbing nodes, which gives a preliminary saliency measure. Thirdly, a refined saliency result is generated in a similar way but with foreground nodes extracted from the preliminary saliency map as absorbing nodes, which inhibits the background and efficiently enhances salient foreground regions. Finally, a Laplacian-based smoothing procedure is utilized to spread the patch saliency to each vertex. Experimental results demonstrate that our scheme performs competitively against the state-of-the-art approaches.