Kernel biased discriminant analysis using histogram intersection kernel for content-based image retrieval

Lin Mei, Gerd Brunner, Lokesh Setia, Hans Burkhardt

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

Abstract

It is known that no single descriptor is powerful enough to encompass all aspects of image content, i.e. each feature extraction method has its own view of the image content. A possible approach to cope with that fact is to get a whole view of the image(object). Then using machine learning approach from user's Relevance feedback to obtain a reduced feature. In this paper, we concentrate on some points about Biased Discriminant Analysis / Kernel Biased Discriminant Analysis (BDA/KBDA) based machine learning approach for CBIR. The contributions of this paper are: 1. using generalized singular value decomposition (GSVD) based approach solve the small sample size problem in BDA/KBDA and 2. using histogram intersection as a kernel for KBDA. Experiments show that this kind of kernel gets improvement compare to other common kernels.

Original languageEnglish (US)
Pages (from-to)63-70
Number of pages8
JournalLecture Notes in Computer Science
Volume3578
DOIs
StatePublished - 2005
Event6th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2005 - Brisbane, Australia
Duration: Jul 6 2005Jul 8 2005

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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