NDLI: Saliency and co-saliency detection by low-rank multiscale fusion

Content Provider	IEEE Xplore Digital Library
Author	Rui Huang Wei Feng Jizhou Sun
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. Sci. & Technol., Tianjin Univ., Tianjin, China (Rui Huang; Wei Feng; Jizhou Sun)
Abstract	To facilitate efficiency, most recent successful saliency detection methods are built on superpixel level. However, saliency detection with single-scale superpixel segmentation may fail in capturing the intrinsic salient objects in complex natural scenes with small-scale high-contrast backgrounds. To tackle this problem and realize more reliable saliency detection, we present a simple strategy using multiscale superpixels to jointly detect salient object via low-rank analysis. Specifically, we construct a multiscale superpixel pyramid and derive the corresponding saliency map using multiple saliency features and priors for each single scale at first. Then, we show that by joint low-rank analysis of multiscale saliency maps, we can obtain a more reliable adaptively fused saliency map that takes all scales saliency results into account. We further propose a GMM-based co-saliency prior to enable the above approach to detecting co-salient objects from multiple images. Extensive experiments on benchmark datasets validate the effectiveness and superiority of the proposed approach over state-of-the-art methods.
Starting Page	1
Ending Page	6
File Size	3577697
Page Count	6
File Format	PDF
ISBN	9781479970827
DOI	10.1109/ICME.2015.7177414
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-29
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image color analysis Feature extraction Image segmentation Matrix decomposition Computational modeling Robustness GMM-based co-saliency prior Saliency co-saliency low-rank analysis
Content Type	Text
Resource Type	Article

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