Kernelized Subspace Ranking for Saliency Detection

Abstract

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.