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An adaptive feature fusion framework for multi-class classification based on SVM

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Abstract

An adaptive feature fusion framework is proposed for multi-class classification based on SVM. In a similar manner of one-versus-all (OVA), one of the multi-class SVM schemes, the proposed approach decomposes a multi-class classification into several binary classifications. The main difference lies in that each classifier is created with the most suitable feature vectors to discriminate one class from all the other classes. The feature vectors of the unknown samples are selected by each classifier adaptively such that recognition is fulfilled accordingly. In addition, novel evaluation criterions are defined to deal with the frequent small-number sample problems. A writer recognition experiment is carried out to accomplish this framework with three kinds of feature vectors: texture, structure and morphological features. Finally, the performance of the proposed approach is illustrated as compared with the OVA by applying the same feature vectors for all classes.

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Authors and Affiliations

  1. School of Software, Tsinghua University, Beijing, People’s Republic of China

    Peipei Yin & Chao Wang

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, People’s Republic of China

    Fuchun Sun & Huaping Liu

  3. State Key Laboratory of Intelligent Technology and Systems, Beijing, People’s Republic of China

    Fuchun Sun & Huaping Liu

Authors
  1. Peipei Yin
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  2. Fuchun Sun
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  3. Chao Wang
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  4. Huaping Liu
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Correspondence to Peipei Yin.

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Yin, P., Sun, F., Wang, C. et al. An adaptive feature fusion framework for multi-class classification based on SVM. Soft Comput 12, 685–691 (2008). https://doi.org/10.1007/s00500-007-0250-3

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  • DOI: https://doi.org/10.1007/s00500-007-0250-3

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