Salient object detection for searched web images via global saliency


In this paper, we deal with the problem of detecting the existence and the location of salient objects for thumbnail images on which most search engines usually perform visual analysis in order to handle web-scale images. Different from previous techniques, such as sliding window-based or segmentation-based schemes for detecting salient objects, we propose to use a learning approach, random forest in our solution. Our algorithm exploits global features from multiple saliency indicators to directly predict the existence and the position of the salient object. To validate our algorithm, we constructed a large image database collected from Bing image search, that contains hundreds of thousands of manually labeled web images. The experimental results using this new database and the resized MSRA database [16] demonstrate that our algorithm outperforms previous state-of-the-art methods.

DOI: 10.1109/CVPR.2012.6248054

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@article{Wang2012SalientOD, title={Salient object detection for searched web images via global saliency}, author={Peng Wang and Jingdong Wang and Gang Zeng and Jie Feng and Hongbin Zha and Shipeng Li}, journal={2012 IEEE Conference on Computer Vision and Pattern Recognition}, year={2012}, pages={3194-3201} }