Papers › Saliency Pattern Detection by Ranking Structured Trees
Saliency Pattern Detection by Ranking Structured Trees
Lei Zhu, Haibin Ling, Jin Wu, Huiping Deng, Jin Liu
In this paper we propose a new salient object detection method via structured label prediction. By learning appearance features in rectangular regions, our structural region representation encodes the local saliency distribution with a matrix of binary labels. We show that the linear combination of structured labels can well model the saliency distribution in local regions. Representing region saliency with structured labels has two advantages: 1) it connects the label assignment of all enclosed pixels, which produces a smooth saliency prediction; and 2) regular-shaped nature of structured labels enables well definition of traditional cues such as regional properties and center surround contrast, and these cues help to build meaningful and informative saliency measures. To measure the consistency between a structured label and the corresponding saliency distribution, we further propose an adaptive label ranking algorithm using proposals that are generated by a CNN model. Finally, we introduce a K-NN enhanced graph representation for saliency propagation, which is more favorable for our task than the widely-used adjacent-graph-based ones. Experimental results demonstrate the effectiveness of our proposed method on six popular benchmarks compared with state-of-the-art approaches.
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