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Indexed by:会议论文

Date of Publication:2013-09-15

Included Journals:EI、CPCI-S、Scopus

Page Number:1772-1776

Key Words:Object level image saliency; hierarchical segmentation; random walks; heat diffusion

Abstract: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.