点击次数：

发布时间：2019-03-10

论文类型：会议论文

发表时间：2016-01-01

收录刊物：SCIE、CPCI-S

卷号：9912

页面范围：450-466

关键字：Saliency detection; Subspace ranking; Feature projection

摘要：In this paper, we propose a novel saliency method that takes advantage of object-level proposals and region-based convolutional neural network (R-CNN) features. We follow the learning-to-rank methodology, and solve a ranking problem satisfying the constraint that positive samples have higher scores than negative ones. As the dimensionality of the deep features is high and the amount of training data is low, ranking in the primal space is suboptimal. A new kernelized subspace ranking model is proposed by jointly learning a Rank-SVM classifier and a subspace projection. The projection aims to measure the pairwise distances in a low-dimensional space. For an image, the ranking score of each proposal is assigned by the learnt ranker. The final saliency map is generated by a weighted fusion of the top-ranked candidates. Experimental results show that the proposed algorithm performs favorably against the state-of-the-art methods on four benchmark datasets.