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The Semi-explicit Shape Model for Multi-object Detection and Classification

  • Simon Polak
  • Amnon Shashua
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6312)

Abstract

We propose a model for classification and detection of object classes where the number of classes may be large and where multiple instances of object classes may be present in an image. The algorithm combines a bottom-up, low-level, procedure of a bag-of-words naive Bayes phase for winnowing out unlikely object classes with a high-level procedure for detection and classification. The high-level process is a hybrid of a voting method where votes are filtered using beliefs computed by a class-specific graphical model. In that sense, shape is both explicit (determining the voting pattern) and implicit (each object part votes independently) — hence the term ”semi-explicit shape model”.

Keywords

Training Image Interest Point Object Class Marginal Probability Code Word 
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 2010

Authors and Affiliations

  • Simon Polak
    • 1
  • Amnon Shashua
    • 1
  1. 1.School of Computer Science and EngineeringThe Hebrew University of Jerusalem 

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