NDLI: Object level image saliency by hierarchical segmentation

Content Provider	IEEE Xplore Digital Library
Author	Zhenzhen Zhang Junjie Cao Guangyu Zhong Wangyi Liu Zhixun Su
Copyright Year	2013
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.
Starting Page	1772
Ending Page	1776
File Size	2879213
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738365
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	heat diffusion Object level image saliency hierarchical segmentation random walks
Content Type	Text
Resource Type	Article

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