Top-Down Saliency Detection via Contextual Pooling

  • Authors:

  • Jun Zhu;Yuanyuan Qiu;Rui Zhang;Jun Huang;Wenjun Zhang

  • Affiliations:

  • Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China;Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China;Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China;Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, People's Republic of China;Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China

  • Venue:
  • Journal of Signal Processing Systems
  • Year:
  • 2014

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Abstract

Goal-driven top-down mechanism plays important role in the case of object detection and recognition. In this paper, we propose a top-down computational model for goal-driven saliency detection based on the coding-based classification framework. It consists of four successive steps: feature extraction, descriptor coding, contextual pooling and saliency prediction. Particularly, we investigate the effect of spatial context information for saliency detection, and propose a block-wise spatial pooling operation to take advantage of contextual cues in multiple neighborhood scales and orientations. The experimental results on three datasets demonstrate that our method can effectively exploit contextual information and achieves the state-of-the-art performance on top-down saliency detection task.