Saliency Detection by Compactness Diffusion

Qi Zheng, Peng Zhang and Xinge You

Abstract

Most existing methods of salient object segmentation only focus on foreground cues such as contrast, or background cues such as boundary connectivity. Another problem is that they have used redundant information to generate an acceptable saliency map such as variances in different color spaces, multi-scale features and so on. In this paper, we propose saliency detecting with a diffusion model; use optimal seeds generated from foreground statistic cue, i.e., the compactness. Each superpixel is considered as a node and a fully connected graph is constructed to calculate the global compactness of each node. Then the local connected graph is constructed by only considering adjacent nodes, and compactness is diffused by applying a quadratic energy model to generate a coarse saliency map. After that, boundary prior is combined with the coarse saliency map for further eliminating the background. Experiments on three benchmark datasets including MSRA 1000, ECSSD and DUT-OMRON show that compared with other seven state-of-the-art methods, our model achieves stable and excellent performance.

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DOI

10.5244/C.31.68
https://dx.doi.org/10.5244/C.31.68

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Bibtex

            @inproceedings{BMVC2017_68,
                title={Saliency Detection by Compactness Diffusion},
                author={Qi Zheng, Peng Zhang and Xinge You},
                year={2017},
                month={September},
                pages={68.1-68.12},
                articleno={68},
                numpages={12},
                booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
                publisher={BMVA Press},
                editor={Tae-Kyun Kim, Stefanos Zafeiriou, Gabriel Brostow and Krystian Mikolajczyk},
                doi={10.5244/C.31.68},
                isbn={1-901725-60-X},
                url={https://dx.doi.org/10.5244/C.31.68}
            }