Wavelet feature selection for image classification.

Full Abstract

Energy distribution over wavelet subbands is a widely used feature for wavelet packet based texture classification. Due to the overcomplete nature of the wavelet packet decomposition, feature selection is usually applied for a better classification accuracy and a compact feature representation. The majority of wavelet feature selection algorithms conduct feature selection based on the evaluation of each subband separately, which implicitly assumes that the wavelet features from different subbands are independent. In this paper, the dependence between features from different subbands is investigated theoretically and simulated for a given image model. Based on the analysis and simulation, a wavelet feature selection algorithm based on statistical dependence is proposed. This algorithm is further improved by combining the dependence between wavelet feature and the evaluation of individual feature component. Experimental results show the effectiveness of the proposed algorithms in incorporating dependence into wavelet feature selection.

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Author information

Author/s: Huang, Ke (K); Aviyente, Selin (S);

Affiliation: Department of Electrical and Computer Engineering, Michigan State University, MI 48824, USA. kehuang@gmail.com

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society (IEEE Trans Image Process), published in United States. (Language: eng)

Reference: 2008-Sep; vol 17 (issue 9) : pp 1709-20

Dates: Created 2008/08/20; Completed 2008/09/23;

PMID: 18713675, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Artificial Intelligence
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods

Image Interpretation, Computer-Assisted - methods
Pattern Recognition, Automated - methods
Reproducibility of Results
Sensitivity and Specificity

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