International Journal of Computer Vision

, Volume 123, Issue 2, pp 251–268 | Cite as

Salient Object Detection: A Discriminative Regional Feature Integration Approach

  • Jingdong Wang
  • Huaizu Jiang
  • Zejian Yuan
  • Ming-Ming Cheng
  • Xiaowei Hu
  • Nanning Zheng
Article

Abstract

Feature integration provides a computational framework for saliency detection, and a lot of hand-crafted integration rules have been developed. In this paper, we present a principled extension, supervised feature integration, which learns a random forest regressor to discriminatively integrate the saliency features for saliency computation. In addition to contrast features, we introduce regional object-sensitive descriptors: the objectness descriptor characterizing the common spatial and appearance property of the salient object, and the image-specific backgroundness descriptor characterizing the appearance of the background of a specific image, which are shown more important for estimating the saliency. To the best of our knowledge, our supervised feature integration framework is the first successful approach to perform the integration over the saliency features for salient object detection, and outperforms the integration approach over the saliency maps. Together with fusing the multi-level regional saliency maps to impose the spatial saliency consistency, our approach significantly outperforms state-of-the-art methods on seven benchmark datasets. We also discuss several followup works which jointly learn the representation and the saliency map using deep learning.

Keywords

Salient object detection Data-driven 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Microsoft ResearchBeijingPeople’s Republic of China
  2. 2.University of MassachusettsAmherstUSA
  3. 3.Xi’an Jiaotong UniversityXi’anPeople’s Republic of China
  4. 4.CCCE & CSNankai UniversityTianjinPeople’s Republic of China

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