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

scholarly journals Information-Theoretic Term Selection for New Item Recommendation

2014 ◽  
pp. 236-243 ◽  
Author(s):  
Thales F. Costa ◽  
Anisio Lacerda ◽  
Rodrygo L. T. Santos ◽  
Nivio Ziviani
Keyword(s):  
Information Theoretic ◽  
Term Selection ◽  
Theoretic Term ◽  
Selection For ◽  
New Item

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.

Information-Theoretic Feature Selection for Classification

2007 ◽  
Author(s):  
Alok A. Joshi ◽  
Scott M. James ◽  
Peter H. Meckl ◽  
Galen B. King ◽  
Kristofer Jennings
Keyword(s):  
Feature Selection ◽  
Information Theoretic ◽  
Selection For
Sign in / Sign up

Export Citation Format

Share Document