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Adding Discriminative Power to Hierarchical Compositional Models for Object Class Detection

  • Matej Kristan
  • Marko Boben
  • Domen Tabernik
  • Ales Leonardis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)

Abstract

In recent years, hierarchical compositional models have been shown to possess many appealing properties for the object class detection such as coping with potentially large number of object categories. The reason is that they encode categories by hierarchical vocabularies of parts which are shared among the categories. On the downside, the sharing and purely reconstructive nature causes problems when categorizing visually-similar categories and separating them from the background. In this paper we propose a novel approach that preserves the appealing properties of the generative hierarchical models, while at the same time improves their discrimination properties. We achieve this by introducing a network of discriminative nodes on top of the existing generative hierarchy. The discriminative nodes are sparse linear combinations of activated generative parts. We show in the experiments that the discriminative nodes consistently improve a state-of-the-art hierarchical compositional model. Results show that our approach considers only a fraction of all nodes in the vocabulary (less than 10%) which also makes the system computationally efficient.

Keywords

compositional models hierarchical models categorization discriminative parts 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Matej Kristan
    • 1
  • Marko Boben
    • 1
  • Domen Tabernik
    • 1
  • Ales Leonardis
    • 2
    • 1
  1. 1.Faculty of Computer and Information ScienceUniversity of LjubljanaSlovenia
  2. 2.CN-CR Centre, School of Computer ScienceUniversity of BirminghamUK

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