Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 22, Issue 5, Pages 588-592Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2014.2364896
Keywords
Co-saliency detection; efficient manifold ranking; fusion; saliency model
Categories
Funding
- National Natural Science Foundation of China [61273258, 61171144]
- 973 Plan of China [2015CB856004]
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This letter proposes a novel framework to detect common salient objects in a group of images automatically and efficiently. Different from most existing co-saliency models which directly redesign algorithms for multiple images, the saliency model for a single image is fully exploited under the proposed framework to guide the co-saliency detection. Given single image saliency maps, a two-stage guided detection pipeline led by queries is proposed to obtain the guided saliency maps of the image set through a ranking scheme. Then the guided saliency maps generated by different queries are fused in a way that takes advantages of both averaging and multiplication. The proposed model makes existing saliency models work well in co-saliency scenarios. Experimental results on two benchmark databases demonstrate that the proposed framework outperforms the state-of-the-art models in terms of both accuracy and efficiency.
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