戚金清
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论文类型:期刊论文
发表时间:2017-01-01
发表刊物:IEEE TRANSACTIONS ON IMAGE PROCESSING
收录刊物:SCIE、EI
卷号:26
期号:1
页面范围:414-425
ISSN号:1057-7149
关键字:Saliency detection; weak saliency model; strong saliency model; co-bootstrapping
摘要:In this paper, we propose a visual saliency detection algorithm to explore the fusion of various saliency models in a manner of bootstrap learning. First, an original bootstrapping model, which combines both weak and strong saliency models, is constructed. In this model, image priors are exploited to generate an original weak saliency model, which provides training samples for a strong model. Then, a strong classifier is learned based on the samples extracted from the weak model. We use this classifier to classify all the salient and non-salient superpixels in an input image. To further improve the detection performance, multi-scale saliency maps of weak and strong model are integrated, respectively. The final result is the combination of the weak and strong saliency maps. The original model indicates that the overall performance of the proposed algorithm is largely affected by the quality of weak saliency model. Therefore, we propose a co-bootstrapping mechanism, which integrates the advantages of different saliency methods to construct the weak saliency model thus addresses the problem and achieves a better performance. Extensive experiments on benchmark data sets demonstrate that the proposed algorithm outperforms the stateof- the-art methods.