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Cited by 2,976

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Global contrast based salient region detection

Cheng, Ming-Ming, Zhang, Guo-Xin, Mitra, Niloy J., Huang, Xiaolei, Hu, Shi-Min·2011·CVPR 2011
DOI 10.1109/cvpr.2011.5995344
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Abstract

Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.

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Research works printed but not linked: 31

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Cited by

Metadata

Authors
Cheng, M.-M., Zhang, G.-X., Mitra, N. J., Huang, X., & Hu, S.-M.
Venue
CVPR 2011
Publisher
IEEE
Published
2011-06
Volume
37
Issue
3
Pages
409-416
DOI
10.1109/cvpr.2011.5995344
OpenAlex
W2037954058
Cited by
2,976
References
4
FWCI
141.81
Type
conference paper
Language
en
Open access
closed

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