Mesh Saliency Detection via Double Absorbing Markov Chain in Feature Space

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.

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Recommended citation: Xiuping Liu, Pingping Tao, Junjie Cao, He Chen, Changqing Zou. (2016). "Mesh Saliency Detection via Double Absorbing Markov Chain in Feature Space." The Visual Computer.
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