NDLI: Saliency Detection via Dense and Sparse Reconstruction

Content Provider	IEEE Xplore Digital Library
Author	Xiaohui Li Huchuan Lu Lihe Zhang Xiang Ruan Ming-Hsuan Yang
Copyright Year	2013
Description	Author affiliation: Univ. of California at Merced, Merced, CA, USA (Ming-Hsuan Yang)
Abstract	In this paper, we propose a visual saliency detection algorithm from the perspective of reconstruction errors. The image boundaries are first extracted via super pixels as likely cues for background templates, from which dense and sparse appearance models are constructed. For each image region, we first compute dense and sparse reconstruction errors. Second, the reconstruction errors are propagated based on the contexts obtained from K-means clustering. Third, pixel-level saliency is computed by an integration of multi-scale reconstruction errors and refined by an object-biased Gaussian model. We apply the Bayes formula to integrate saliency measures based on dense and sparse reconstruction errors. Experimental results show that the proposed algorithm performs favorably against seventeen state-of-the-art methods in terms of precision and recall. In addition, the proposed algorithm is demonstrated to be more effective in highlighting salient objects uniformly and robust to background noise.
Sponsorship	IEEE Comput. Soc.
Starting Page	2976
Ending Page	2983
File Size	1023572
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.370
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image reconstruction Image segmentation Measurement uncertainty Bayes methods Computational modeling Databases Visualization
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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