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Efficient Saliency-Model-Guided Visual Co-Saliency Detection
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DOI：10.1109/LSP.2014.2364896.png)
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摘要
En 中文
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.
Keyword:
Co-saliency detection
efficient manifold ranking
fusion
saliency model
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期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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