NDLI: Co-saliency detection based on region-level fusion and pixel-level refinement

Content Provider	IEEE Xplore Digital Library
Author	Lina Li Zhi Liu Wenbin Zou Xiang Zhang Le Meur, O.
Copyright Year	2014
Description	Author affiliation: Univ. of Rennes 1, Rennes, France (Le Meur, O.) \|\| Sch. of Electron. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China (Xiang Zhang) \|\| Coll. of Inf. Eng., Shenzhen Univ., Shenzhen, China (Wenbin Zou) \|\| Sch. of Commun. & Inf. Eng., Shanghai Univ., Shanghai, China (Lina Li; Zhi Liu)
Abstract	This paper addresses the problem of co-saliency detection, which aims to identify the common salient objects in a set of images and is important for many applications such as object co-segmentation and co-recognition. First, the segmentation driven low-rank matrix recovery model is used for intra saliency detection in each individual image of the image set, to highlight the regions whose features are sparse in each image. Then, a region-level fusion method, which exploits inter-region dissimilarities on color histograms and global consistency of regions over the image set, adjusts the intra saliency maps to obtain the region-level co-saliency maps, which can highlight co-salient object regions and suppress irrelevant regions. Finally, a pixel-level refinement method, which integrates color-spatial similarity between pixel and region with image border connectivity based object prior, generates the pixel-level co-saliency maps with better quality. Extensive experiments on two benchmark datasets demonstrate that the proposed co-saliency model consistently outperforms the state-of-the-art co-saliency models in both subjective and objective evaluation.
Starting Page	1
Ending Page	6
File Size	1087298
Page Count	6
File Format	PDF
ISBN	9781479947614
DOI	10.1109/ICME.2014.6890183
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Image color analysis Educational institutions Computational modeling Benchmark testing Histograms Predictive models pixel-level refinement Co-saliency detection saliency model region-level fusion
Content Type	Text
Resource Type	Article

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