Texture classification using dominant wavelet packet energy features

2002 ◽  
Author(s):  
Moon-Chuen Lee ◽  
Chi-Man Pun
Keyword(s):  
Texture Classification ◽  
Wavelet Packet

Information-theoretic wavelet packet subband selection for texture classification

2006 ◽  
Vol 86 (7) ◽  
pp. 1410-1420 ◽  
Author(s):  
Ke Huang ◽  
Selin Aviyente
Keyword(s):  
Texture Classification ◽  
Wavelet Packet ◽  
Information Theoretic ◽  
Selection For

Efficient and Adaptive Rotation Invariant Wavelet Transform

2003 ◽  
Vol 01 (03) ◽  
pp. 353-372
Author(s):  
Chi-Man Pun
Keyword(s):  
Wavelet Transform ◽  
Texture Classification ◽  
Wavelet Packet ◽  
Sampling Rate ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
Information Cost ◽  
Image Size ◽  
Rotation Invariant ◽  
Adaptive Wavelet

It is well known that the sensitivity to translations and orientations is a major drawback in 2D discrete wavelet transform (DWT). In this paper, we have proposed an effective scheme for rotation invariant adaptive wavelet packet transform. During decomposition, the wavelet coefficients are obtained by applying a polar transform (PT) followed by a row-shift invariant wavelet packet decomposition (RSIWPD). In the first stage, the polar transform generates a row-shifted image and is adaptive to the image size to achieve complete and minimum sampling rate. In the second stage, the RSIWPD is applied to the row-shifted image to generate rotation invariant but over completed subbands of wavelet coefficients. In order to reduce the redundancy and computational complexity, we adaptively select some subbands to decompose and form a best basis representation with minimal information cost with respect to an appropriate information cost function. With this best basis representation, the original image can be reconstructed easily by applying a row-shift invariant wavelet packet reconstruction (RSIWPR) followed by an inverse polar transform (IPT). In the experiments, we study the application of this representation for texture classification and achieve 96.5% classification accuracy.

scholarly journals Local discriminant wavelet packet basis for texture classification

2003 ◽  
Author(s):  
Nasir Rajpoot
Keyword(s):  
Texture Classification ◽  
Wavelet Packet

Texture Classification Using Wavelet Packet and Fourier Transforms.

1995 ◽  
Author(s):  
Xiaoou Tang ◽  
W. K. Stewart
Keyword(s):  
Fourier Transforms ◽  
Texture Classification ◽  
Wavelet Packet

Pit Pattern Classification Using Multichannel Features and Multiclassification

2009 ◽  
pp. 335-350 ◽  
Author(s):  
Michael Haefner ◽  
Alfred Gangl ◽  
Michael Liedlgruber ◽  
A. Uhl ◽  
Andreas Vecsei ◽  
...  
Keyword(s):  
Pattern Classification ◽  
Texture Classification ◽  
Wavelet Packet ◽  
Structural Features ◽  
Spatial Domain ◽  
Classification Methods ◽  
Statistical Features ◽  
Pit Pattern ◽  
Color Channel ◽  
Pit Pattern Classification

Wavelet-, Fourier-, and spatial domain-based texture classification methods have been used successfully for classifying zoom-endoscopic colon images according to the pit pattern classification scheme. Regarding the wavelet-based methods, statistical features based on the wavelet coefficients as well as structural features based on the wavelet packet decomposition structures of the images have been used. In the case of the Fourier-based method, statistical features based on the Fourier-coefficients in ring filter domains are computed. In the spatial domain, histogram-based techniques are used. After reviewing the various methods employed we start by extracting the feature vectors for the methods from one color channel only. To enhance the classification results the methods are then extended to utilize multichannel features obtained from all three color channels of the respective color model used. Finally, these methods are combined into one multiclassifier to stabilize classification results across the image classes.

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