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Cooperative Object Segmentation and Behavior Inference in Image Sequences

Authors
  • Gui, Laura1
  • Thiran, Jean-Philippe1
  • Paragios, Nikos2
  • 1 Ecole Polytechnique Fédérale de Lausanne, Signal Processing Institute, Lausanne, Switzerland , Lausanne (Switzerland)
  • 2 Ecole Centrale de Paris, Laboratoire MAS, Chatenay-Malabry, France , Chatenay-Malabry (France)
Type
Published Article
Journal
International Journal of Computer Vision
Publisher
Springer-Verlag
Publication Date
Jun 17, 2008
Volume
84
Issue
2
Pages
146–162
Identifiers
DOI: 10.1007/s11263-008-0146-4
Source
Springer Nature
Keywords
License
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Abstract

In this paper, we propose a general framework for fusing bottom-up segmentation with top-down object behavior inference over an image sequence. This approach is beneficial for both tasks, since it enables them to cooperate so that knowledge relevant to each can aid in the resolution of the other, thus enhancing the final result. In particular, the behavior inference process offers dynamic probabilistic priors to guide segmentation. At the same time, segmentation supplies its results to the inference process, ensuring that they are consistent both with prior knowledge and with new image information. The prior models are learned from training data and they adapt dynamically, based on newly analyzed images. We demonstrate the effectiveness of our framework via particular implementations that we have employed in the resolution of two hand gesture recognition applications. Our experimental results illustrate the robustness of our joint approach to segmentation and behavior inference in challenging conditions involving complex backgrounds and occlusions of the target object.

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