Saliency ranker: A new salient object detection method

Abstract

Recently, saliency detection has become an active research topic in learning from labeled image, where various supervised methods were designed. Many existing methods usually cast saliency detection as a binary classification or regression problem, in which saliency detection performance relies heavily on the expensive pixel-wise annotations of salient objects. This paper addresses the issue by developing a novel learning-to-rank model with a limited number of training data, which combines the strength of cost-sensitive label ranking methods with the power of low-rank matrix recovery theories. Rather than using a binary decision for each saliency value, our approach ranks saliency values in a descending order with the estimated relevance to the given saliency. Additionally, we also aggregate the prediction models for different saliency labels into a matrix, and solve saliency ranking via a low-rank matrix recovery problem. Extensive experiments over challenging benchmarks clearly validate advantage of our method.