Automatic salient object segmentation based on context and shape prior

Citations Over TimeTop 1% of 2011 papers

Abstract

We propose a novel automatic salient object segmentation algorithm which integrates both bottom-up salient stimuli and object-level shape prior, i.e., a salient object has a well-defined closed boundary. Our approach is formalized as an iterative energy minimization framework, leading to binary segmentation of the salient object. Such energy minimization is initialized with a saliency map which is computed through context analysis based on multi-scale superpixels. Object-level shape prior is then extracted combining saliency with object boundary information. Both saliency map and shape prior update after each iteration. Experimental results on two public benchmark datasets show that our proposed approach outperforms state-of-the-art methods.

Related Papers

• → Image Segmentation Technology Based on Genetic Algorithm(2019)11 cited
• → Medical Image Segmentation(2013)11 cited
• → A real-time parallel combination segmentation method for aluminum surface defect images(2015)8 cited
• → Energy minimization for the flow in ducts and networks(2014)5 cited
• → An energy minimization method for matching and comparing structured object representations(1997)1 cited