Texture Classification and Retrieval Using Shearlets and Linear Regression

Statistical modeling of wavelet subbands has frequently been used for image recognition and retrieval. However, traditional wavelets are unsuitable for use with images containing distributed discontinuities, such as edges. Shearlets are a newly developed extension of wavelets that are better suited...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on cybernetics 2015-03, Vol.45 (3), p.358-369
Main Authors: Dong, Yongsheng, Tao, Dacheng, Li, Xuelong, Ma, Jinwen, Pu, Jiexin
Format: Article
Language:English
Subjects:
Classification
Computational modeling
Contourlets
Image recognition
Linear regression
Regression analysis
shearlets
subband dependence
texture classification
texture retrieval
Training
Vectors
Wavelet transforms
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Statistical modeling of wavelet subbands has frequently been used for image recognition and retrieval. However, traditional wavelets are unsuitable for use with images containing distributed discontinuities, such as edges. Shearlets are a newly developed extension of wavelets that are better suited to image characterization. Here, we propose novel texture classification and retrieval methods that model adjacent shearlet subband dependences using linear regression. For texture classification, we use two energy features to represent each shearlet subband in order to overcome the limitation that subband coefficients are complex numbers. Linear regression is used to model the features of adjacent subbands; the regression residuals are then used to define the distance from a test texture to a texture class. Texture retrieval consists of two processes: the first is based on statistics in contourlet domains, while the second is performed using a pseudo-feedback mechanism based on linear regression modeling of shearlet subband dependences. Comprehensive validation experiments performed on five large texture datasets reveal that the proposed classification and retrieval methods outperform the current state-of-the-art.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2014.2326059