An Incremental Structured Part Model for Image Classification

  • Huigang Zhang
  • Xiao Bai
  • Jian Cheng
  • Jun Zhou
  • Huijie Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)

Abstract

The state-of-the-art image classification methods usually require many training samples to achieve good performance. To tackle this problem, we present a novel incremental method in this paper, which learns a part model to classify objects using only a small number of training samples. Our model captures the inherent connections of the semantic parts of objects and builds structural relationship between them. In the incremental learning stage, we use high entropy images that have been accepted by users to update the learned model. The proposed approach is evaluated on two datasets, which demonstrates its advantages over several alternative classification methods in the literature.

Keywords

Image classification semantic parts structural relationship incremental learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Huigang Zhang
    • 1
  • Xiao Bai
    • 1
  • Jian Cheng
    • 2
  • Jun Zhou
    • 3
  • Huijie Zhao
    • 1
  1. 1.School of Computer Science and EngineeringBeihang UniversityBeijingChina
  2. 2.Institute of Automation Chinese Academy of SciencesBeijingChina
  3. 3.School of Information and Communication TechnologyGriffith UniversityNathanAustralia

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