Integrating manifold ranking with boundary expansion and corners clustering for saliency detection of home scene

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In this paper, we propose a novel framework for saliency detection of home scene by exploiting manifold ranking, boundary expansion, and corners clustering. Our proposed method firstly combines color cues in RGB and CIELab to select image boundary seeds, and exclude the ones which might be located at salient objects as much as possible. Then, we utilize the boundary seeds on each image boundary as the queries of manifold ranking to compute saliency and integrate them for a background-based saliency map. For the foreground-based saliency detection. Boundary expansion combined with background-based saliency map highlights foreground regions, which are regarded as queries for a foreground-based saliency map. Moreover, we achieve center prior saliency map through multi-scale Harris corner detection and corners clustering to further highlight salient regions and suppress background regions. Finally, we integrate the three saliency maps via the proposed unified framework for a more accurate and smooth saliency map. Both qualitative and quantitative experimental results indicate that our proposed method can deliver better performance than several state-of-the-art saliency detection methods as a whole.