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Object Co-detection

  • Sid Yingze Bao
  • Yu Xiang
  • Silvio Savarese
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)

Abstract

In this paper we introduce a new problem which we call object co-detection. Given a set of images with objects observed from two or multiple images, the goal of co-detection is to detect the objects, establish the identity of individual object instance, as well as estimate the viewpoint transformation of corresponding object instances. In designing a co-detector, we follow the intuition that an object has consistent appearance when observed from the same or different viewpoints. By modeling an object using state-of-the-art part-based representations such as [1,2], we measure appearance consistency between objects by comparing part appearance and geometry across images. This allows to effectively account for object self-occlusions and viewpoint transformations. Extensive experimental evaluation indicates that our co-detector obtains more accurate detection results than if objects were to be detected from each image individually. Moreover, we demonstrate the relevance of our co-detection scheme to other recognition problems such as single instance object recognition, wide-baseline matching, and image query.

Keywords

Training Image Object Detection Query Image Object Instance Part Representation 
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-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sid Yingze Bao
    • 1
  • Yu Xiang
    • 1
  • Silvio Savarese
    • 1
  1. 1.University of Michigan at Ann ArborUSA

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