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Combining LVQ with SVM technique for image semantic annotation

  • ICONIP2010
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Abstract

When support vector machine (SVM) classifier is applied to image semantic annotation, it usually encounters the problem of excessive training samples. In this paper, we propose a novel method, which is by combining learning vector quantization (LVQ) technique and SVM classifier, to improve annotation accuracy and speed. Affinity propagation algorithm-based LVQ technique is used to optimize the training set, and a few number of optimized representative feature vectors are used to train SVM. This approach not only meets the small sample size characteristic of SVM, but also greatly accelerates the training and annotating process. Comparative experimental studies confirm the validity of the proposed method.

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Acknowledgments

The research work described in this paper was fully supported by the grants from the National Natural Science Foundation of China (Project No. 90820010, 60911130513)

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Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Ping Guo, Ziheng Jiang, Song Lin & Yao Yao

Authors
  1. Ping Guo
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  2. Ziheng Jiang
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  3. Song Lin
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  4. Yao Yao
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Corresponding author

Correspondence to Ping Guo.

Additional information

This work is an extended version of the paper presented at the 2010 International Conference on Neural Information Processing (ICONIP) [1].

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Guo, P., Jiang, Z., Lin, S. et al. Combining LVQ with SVM technique for image semantic annotation. Neural Comput & Applic 21, 735–746 (2012). https://doi.org/10.1007/s00521-011-0651-1

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