A hybrid feature dimension reduction approach for image classification

Qi Tian, Jie Yu, Ting Rui, Thomas S. Huang

Research output: Contribution to journalConference articlepeer-review


In content-based image retrieval (CBIR), in order to alleviate learning in the high-dimensional space, Fisher discriminant analysis (FDA) and multiple discriminant analysis (MDA) are commonly used to find an optimal discriminating subspace that the data are clustered in the reduced feature space, in which the probabilistic structure of the data could be simplified and captured by simpler model assumption, e.g., Gaussian mixtures. However, due to the two reasons (i) the real number of classes in the image database is usually unknown; and (ii) the image retrieval system acts as a classifier to divide the images into two classes, relevant and irrelevant, the effective dimension of projected subspace is usually one. In this paper, a novel hybrid feature dimension reduction technique is proposed to construct descriptive and discriminant features at the same time by maximizing the Rayleigh coefficient. The hybrid LDA and PCA analysis not only increases the effective dimension of the projected subspace, but also offers more flexibility and alternatives to LDA and PCA. Extensive tests on benchmark and real image databases have shown the superior performance of the hybrid analysis.

Original languageEnglish (US)
Article number02
Pages (from-to)13-24
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2004
EventInternet Multimedia Management Systems V - Philadelphia, PA, United States
Duration: Oct 26 2004Oct 28 2004


  • Dimension reduction
  • Hybrid analysis
  • Image classification
  • LDA
  • PCA

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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