Multi-Saliency Detection via Instance Specific Element Homology
Li Juan Zhou, Jianwu Fang, Yongfeng Ju, Jianru Xue · 2017
Saliency detection aims to find the useful and attractive regions from an image. In many situations, there may be multiple objects in the image, and these objects may have equal attractiveness. Moreover, the appearance of pixels in one object may demonstrate large difference, which could lead to lose the object integrality when detecting saliency. To this end, this paper proposes a multi-saliency detection model via Instance Specific Element Homology (ISEH), where the integrality of an object is also considered. The ISEH is mainly formulated as a belongingness probability computation of two elements (e.g., pixels or superpixels) relative to the same object, which exploits the linking of elements within certain proposal, as well as the objectness of the proposal simultaneously. This work takes ISEH into account in the foreground and background estimation and a final optimization, and generates a superior performance for multi-saliency detection than the state-of-the-arts.