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发布时间：2019-03-11
论文类型：会议论文
发表时间：2013-09-15
收录刊物：Scopus、CPCI-S、EI
页面范围：1772-1776
关键字：Object level image saliency; hierarchical segmentation; random walks; heat diffusion
摘要：Conventional saliency detection approaches are human fixation detection and single dominant region detection. However, real-world photographs usually consist of multiple dominant regions. We propose a saliency detection method with the aim to highlight objects as a whole and distinguish objects with different saliency levels. It combines the bottom-up approach and top-down approach via two nested levels of hierarchical segmentations - the coarse level objects and fine level details. We first calculate a preliminary saliency on the fine patches with a random walk model. Then a location cue and an object-level cue are fused to refine the preliminary saliency to emphasize the objects against the background. At last, the object-level saliency map is synthesized via a heat diffusion process restricted by the coarse level patches to enhance object saliency and distinguish saliency between different objects. Extensive evaluation on a publicly available database verifies that our method outperforms the state-of-the-art algorithms.