Huaizu Jiang1     Jingdong Wang2    Zejian Yuan1     Yang Wu3     Nanning Zheng1     Shipeng Li2
1Xi'an Jiaotong University     2 Microsoft Research Asia     3 Kyoto University
Salient object detection has been attracting a lot of interest, and recently various heuristic computational models have been designed. In this paper, we regard saliency map computation as a regression problem. Our method, which is based on multi-level image segmentation, uses the supervised learning approach to map the regional feature vector to a saliency score, and finally fuses the saliency scores across multiple levels, yielding the saliency map. The contributions lie in two-fold. One is that we show our approach, which integrates the regional contrast, regional property and regional backgroundness descriptors together to form the master saliency map, is able to produce superior saliency maps to existing algorithms most of which combine saliency maps heuristically computed from different types of features. The other is that we introduce a new regional feature vector, backgroundness, to characterize the background, which can be regarded as a counterpart of the objectness descriptor. The performance evaluation on several popular benchmark data sets validates that our approach outperforms existing state-of-the-arts.
Huaizu Jiang, Jingdong Wang, Zejian Yuan, Yang Wu, Nanning Zheng, Shipeng Li. Salient Object Detection: A Discriminative Regional Feature Integration Approach. CVPR, 2013. [PDF][Supplementary Material (coming soon)][Bibtex]
From left to right: input images, results of SVO , CA , CBsal , RC , SF , LRK , and our approach (DRFI), respectively. Please see the paper for details.
Quantitative comparisons of different approaches on benchmark data sets. From left to right: precision-recall curves, ROC curves, and AUC scores, respectively. Please see the paper for details.
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This work was supported in part by the National Basic Research Program of China under Grant No. 2012CB316400, and the National Natural Science Foundation of China under Grant No. 91120006.
Contact: jianghuaizu AT gmail.com