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Learning Contextual Metrics for Automatic Image Annotation

  • Zuotao Liu
  • Xiangdong Zhou
  • Yu Xiang
  • Yan-Tao Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6297)

Abstract

The semantic contextual information is shown to be an important resource for improving the scene and image recognition, but is seldom explored in the literature of previous distance metric learning (DML) for images. In this work, we present a novel Contextual Metric Learning (CML) method for learning a set of contextual distance metrics for real world multi-label images. The relationships between classes are formulated as contextual constraints for the optimization framework to leverage the learning performance. In the experiment, we apply the proposed method for automatic image annotation task. The experimental results show that our approach outperforms the start-of-the-art DML algorithms.

Keywords

Mahalanobis Distance Learning Framework Semantic Context Pairwise Constraint Contextual Constraint 
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

  • Zuotao Liu
    • 1
  • Xiangdong Zhou
    • 1
  • Yu Xiang
    • 1
  • Yan-Tao Zheng
    • 2
  1. 1.Fudan UniversityShanghaiChina
  2. 2.Institute for Infocomm ResearchSingapore

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