Support vector machine pairwise classifiers with error reduction for image classification

ABSTRACT

In this paper we study how Support Vector Machines (SVMs) can be applied to image classification. To enhance classification accuracy, we normalize SVM pairwise classification results. From empirical study on a fifteen-category diversified image set, we show that combining pairwise SVMs and error reduction is an effective approach from image classification. This study is a critical step for our on-going effort on the development of a comprehensive approach, closely adapted to SVMs, to image classification.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Image databases

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

General Terms:
Algorithms, Experimentation, Performance