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2004 Fiscal Year Final Research Report Summary

Adaptive Learning of Classifiers and Its Applications

Research Project

Project/Area Number 15500088
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeSingle-year Grants
Section一般
Research Field Intelligent informatics
Research InstitutionShinshu University

Principal Investigator

MARUYAMA Minoru  Shinshu Univ., Dept.Inf.Eng, Assoc.Prof., 工学部, 助教授 (80283232)

Project Period (FY) 2003 – 2004
Keywordslearning from examples / SVM / boosting / radial basis function / approximation / classification / virtual examples
Research Abstract

To recognize target objects in the image, technique of learning from examples are often exploited. To learn the good classifier, usually a lot of samples are required. On the other hand, human beings can recognize an object efficiently even if it is a novel object. To realize such ability on machines, it is necessary to develop a learning method to give classifiers which are efficient with respect to both recognition rate and recognition speed, from very small learning examples. Classifiers based on the non-linear functions are often used to recognize pattern in a image. Although their classification performance is quite good, their computational cost is often prohibitive. To overcome the difficulty, in this report, we first propose a method to recognize patterns in a image based on the hierarchical classifiers. It consists of two kinds of classifiers : simple and fast classifier, whose performance is not necessarily satisfactory, and the non-linear slow classifier, whose performance is quite good. With the use of first (bottom) classifier, many false targets are rejected. The second, complex classifier is applied to the remaining patterns. By this architecture, fast and reliable classifiers are made possible. As the non-linear classifiers, we use RBF-SVM. In the case of SVM, recognition speed depends on the number of support vectors. To improve the recognition speed further, we propose a method to approximate the SVM based on the sampling from the support vectors. We also propose methods to learn fast and reliable classifiers based on the boosting algorithm subject to the upper bound of the number of RBF centers. To learn the classifiers from the limited number of examples, we propose a method to utilize virtual examples. In the report, we first propose the synthesis method based on the simple geometric transformation. Then, to improve the quality of the samples for hand written pattems, we also propose amethod to generate pattems from online data.

  • Research Products

    (6 results)

All 2005 2004 Other

All Journal Article (6 results)

  • [Journal Article] 階層型識別器を用いた情景画像からの文字抽出手法2005

    • Author(s)
      山口拓磨, 丸山稔
    • Journal Title

      電子情報通信学会論文誌DII (印刷中)

    • Description
      「研究成果報告書概要(和文)」より
  • [Journal Article] Off-line handwritten character recognition by SVM based on the virtual examples synthesized from on-line character2005

    • Author(s)
      M.Maruyama, H.Miyao, Y.Nakano
    • Journal Title

      Proc.International Conference on Document Analysis and Recognition 2005 (ICDAR2005) (to appear)

    • Description
      「研究成果報告書概要(和文)」より
  • [Journal Article] Character extraction from natural scene images by hierarchical classifiers2004

    • Author(s)
      T.Yamaguchi, M.Maruyama
    • Journal Title

      Proc.17th International Conference on Pattern Recognition (ICPR04) 2

      Pages: 687-690

    • Description
      「研究成果報告書概要(和文)」より
  • [Journal Article] Character extraction from natural scene images by hierarchical classifiers2004

    • Author(s)
      T.Yamaguchi, M.Maruyama
    • Journal Title

      Proc.17th International Conference on Pattern Recognition (ICPR04) Vol.2

      Pages: 687-690

    • Description
      「研究成果報告書概要(欧文)」より
  • [Journal Article] Character extraction from natural scene images by hierarchical classifiers

    • Author(s)
      T.Yamaguchi, M.Maruyama
    • Journal Title

      Trans.IEICEJ D-II (to appear)

    • Description
      「研究成果報告書概要(欧文)」より
  • [Journal Article] Off-line handwritten character recognition by SVM based on the virtual examples synthesized from on-line characters

    • Author(s)
      M.Maruyama, H.Miyao, Y.Nakano
    • Journal Title

      Proc.International Conference on Document Analysis and Recognition 2005 (ICDAR2005) (to appear)

    • Description
      「研究成果報告書概要(欧文)」より

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Published: 2006-07-11  

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