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Random walks on graphs to model saliency in images

10 years 11 months ago
Random walks on graphs to model saliency in images
We formulate the problem of salient region detection in images as Markov random walks performed on images represented as graphs. While the global properties of the image are extracted from the random walk on a complete graph, the local properties are extracted from a k-regular graph. The most salient node is selected as the one which is globally most isolated but falls on a compact object. The equilibrium hitting times of the ergodic Markov chain holds the key for identifying the most salient node. The background nodes which are farthest from the most salient node are also identified based on the hitting times calculated from the random walk. Finally, a seeded salient region identification mechanism is developed to identify the salient parts of the image. The robustness of the proposed algorithm is objectively demonstrated with experiments carried out on a large image database annotated with ’ground-truth’ salient regions.
Viswanath Gopalakrishnan, Yiqun Hu, Deepu Rajan
Added 18 May 2010
Updated 25 Aug 2010
Type Conference
Year 2009
Where CVPR
Authors Viswanath Gopalakrishnan, Yiqun Hu, Deepu Rajan
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