Co-occurrence random forests for object localization and classification

  • Authors:

  • Yu-Wu Chu;Tyng-Luh Liu

  • Affiliations:

  • Institute of Information Science, Academia Sinica, Taipei, Taiwan;Institute of Information Science, Academia Sinica, Taipei, Taiwan

  • Venue:
  • ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
  • Year:
  • 2009

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Abstract

Learning techniques based on random forests have been lately proposed for constructing discriminant codebooks for image classification and object localization However, such methods do not generalize well to dealing with weakly labeled data To extend their applicability, we consider incorporating co-occurrence information among image features into learning random forests The resulting classifiers can detect common patterns among objects of the same category, and avoid being trapped by large background patterns that may sporadically appear in the images Our experimental results demonstrate that the proposed approach can effectively handle weakly labeled data and meanwhile derive a more discriminant codebook for image classification.