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RGBD Salient Object Detection: A Benchmark and Algorithms

  • Houwen Peng
  • Bing Li
  • Weihua Xiong
  • Weiming Hu
  • Rongrong Ji
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8691)

Abstract

Although depth information plays an important role in the human vision system, it is not yet well-explored in existing visual saliency computational models. In this work, we first introduce a large scale RGBD image dataset to address the problem of data deficiency in current research of RGBD salient object detection. To make sure that most existing RGB saliency models can still be adequate in RGBD scenarios, we continue to provide a simple fusion framework that combines existing RGB-produced saliency with new depth-induced saliency, the former one is estimated from existing RGB models while the latter one is based on the proposed multi-contextual contrast model. Moreover, a specialized multi-stage RGBD model is also proposed which takes account of both depth and appearance cues derived from low-level feature contrast, mid-level region grouping and high-level priors enhancement. Extensive experiments show the effectiveness and superiority of our model which can accurately locate the salient objects from RGBD images, and also assign consistent saliency values for the target objects.

Keywords

Depth Image Salient Object Salient Region Saliency Detection Visual Saliency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Houwen Peng
    • 1
  • Bing Li
    • 1
  • Weihua Xiong
    • 1
  • Weiming Hu
    • 1
  • Rongrong Ji
    • 2
  1. 1.Institute of AutomationChinese Academy of SciencesChina
  2. 2.Department of Cognitive ScienceXiamen UniversityChina

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