Rotation invariant textural feature extraction for image retrieval using eigen value analysis of intensity gradients and multi-resolution analysis

Authors:
Rahul Das Gupta;Jatindra K. Dash;Mukhopadhyay Sudipta
Affiliations:
-;-;-
Venue:
Pattern Recognition
Year:
2013

Citing 16
Cited 0

A model-based method for rotation invariant texture classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelet transforms and filter banks

Wavelets: a tutorial in theory and applications
Texture Features for Browsing and Retrieval of Image Data

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation and scale invariant texture features using discrete wavelet packet transform

Pattern Recognition Letters
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Radon Transform Orientation Estimation for Rotation Invariant Texture Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation invariant texture classification using LBP variance (LBPV) with global matching

Pattern Recognition
A fast digital Radon transform-An efficient means for evaluating the Hough transform

Pattern Recognition
Rotation invariant texture characterization and retrieval using steerable wavelet-domain hidden Markov models

IEEE Transactions on Multimedia
Texture image retrieval using new rotated complex wavelet filters

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rotation-Invariant Texture Image Retrieval Using Rotated Complex Wavelet Filters

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Texture classification using rotated wavelet filters

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Circular-Mellin features for texture segmentation

IEEE Transactions on Image Processing
Using Zernike moments for the illumination and geometry invariant classification of multispectral texture

IEEE Transactions on Image Processing
Robust rotation-invariant texture classification using a model based approach

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper a novel rotation invariant multi-resolution based texture retrieval technique is proposed. The rotation invariance is achieved by aligning the direction of maximum variation of intensity gradient (defined as principal texture direction) along the reference axis. The principal direction is determined using eigen value analysis of gradient image. Wavelet transform based techniques are applied on the rotated image. The independent representation of textural energies along various directions enhances the retrieval performance over the existing rotation invariant wavelet based techniques which achieve rotation invariance by averaging the direction sensitive components. Extensive experiments on Brodatz database support this postulate.