Paper
21 December 2000 Two-dimensional wavelet-packet-based feature selection method for image recognition
Min-soo Kim, Jang-sun Baek, Soo-hyung Kim, Guee-sang Lee
Author Affiliations +
Proceedings Volume 4307, Document Recognition and Retrieval VIII; (2000) https://doi.org/10.1117/12.410850
Event: Photonics West 2001 - Electronic Imaging, 2001, San Jose, CA, United States
Abstract
We propose a new approach to feature selection for the classification of image data using two-dimensional (2D) wavelet packet bases. To select key features of the image data, the techniques for the dimension reduction are required for which PCA has been most frequently used. However PCA relies on the eigenvalue system, it is not only sensitive to outliers or perturbations but has a tendency to extract only global features. Since the important features for the image data are often characterized by local information such as edges and spikes, PCA does not provide good solutions to such problems. Also eigen value systems usually require high cost in getting the solutions and the complexity of the algorithm is O(n3), where n is the number of variables, or pixels in the original data. In this paper, original image data are transformed into 2D wavelet packet bases and the best discriminant basis is searched to extract relevant features from image data and to discard irrelevant information. In contrast to PCA solutions, properties of wavelets enable the extraction of detail features with global features. Also, the computational complexity of computing the best 2D wavelet packet basis goes down to approximately O(nlog4 n), where n is the number of pixels in the original image data. Experiment results are compared the recognition rates of PCA and our approach to show that the proposed method gives a better results than PCA in most cases.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Min-soo Kim, Jang-sun Baek, Soo-hyung Kim, and Guee-sang Lee "Two-dimensional wavelet-packet-based feature selection method for image recognition", Proc. SPIE 4307, Document Recognition and Retrieval VIII, (21 December 2000); https://doi.org/10.1117/12.410850
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Cited by 2 scholarly publications.
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KEYWORDS
Wavelets

Principal component analysis

Feature selection

Image classification

Time-frequency analysis

Data analysis

Dimension reduction

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