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RGBD Salient Object Detection: A Benchmark and Algorithms

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 8691))

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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.

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Author information

Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, China

    Houwen Peng, Bing Li, Weihua Xiong & Weiming Hu

  2. Department of Cognitive Science, Xiamen University, China

    Rongrong Ji

Authors
  1. Houwen Peng

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  2. Bing Li

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  3. Weihua Xiong

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  4. Weiming Hu

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  5. Rongrong Ji

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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Toront, 6 King’s College Road, M5H 3S5, Toronto, ON, Canada

    David Fleet

  2. Faculty of Electrical Engineering, Department of Cybernetics, Czech Technical University in Prague, Technicka 2, 166 27, Prague 6, Czech Republic

    Tomas Pajdla

  3. Max-Planck-Institut für Informatik, Campus E1 4, 66123, Saarbrücken, Germany

    Bernt Schiele

  4. ESAT - PSI, iMinds, KU Leuven, Kasteelpark Arenberg 10, Bus 2441, 3001, Leuven, Belgium

    Tinne Tuytelaars

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© 2014 Springer International Publishing Switzerland

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Peng, H., Li, B., Xiong, W., Hu, W., Ji, R. (2014). RGBD Salient Object Detection: A Benchmark and Algorithms. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8691. Springer, Cham. https://doi.org/10.1007/978-3-319-10578-9_7

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