Rotation invariant texture characterization using a curvelet based descriptor

Authors:
F. Gómez;E. Romero
Affiliations:
CimaLab, Faculty of Medicine, Universidad Nacional de Colombia, Bogotá DC, Colombia and Coma Science Group, Cyclotron Research Center, University of Liège, Liège, Belgium;CimaLab, Faculty of Medicine, Universidad Nacional de Colombia, Bogotá DC, Colombia
Venue:
Pattern Recognition Letters
Year:
2011

Citing 12
Cited 0

Automated Inspection of Textile Fabrics Using Textural Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study

IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance evaluation in content-based image retrieval: overview and proposals

Pattern Recognition Letters - Special issue on image/video indexing and retrieval
New multiscale transforms, minimum total variation synthesis: applications to edge-preserving image reconstruction

Signal Processing - Image and Video Coding beyond Standards
Texture classification using wavelet transform

Pattern Recognition Letters
Texture Recognition Using a Non-Parametric Multi-Scale Statistical Model

CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Texture classification using Gabor wavelets based rotation invariant features

Pattern Recognition Letters
A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography

Computers in Biology and Medicine
Statistical texture characterization from discrete wavelet representations

IEEE Transactions on Image Processing
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance

IEEE Transactions on Image Processing
Texture analysis and classification with tree-structured wavelet transform

IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.10

Visualization

Abstract

This paper introduces a highly discriminative, precise and simple descriptor of natural textures, based on the curvelet transform. The proposed descriptor is calculated from the statistical pattern of the curvelet coefficients. The image is mapped to the curvelet space, where a statistical parametric model approaches the data distribution for each of the sub-bands. Once these parameters are estimated, they are subband-energy sorted out, achieving thereby the invariance to planar rotations. Finally, the Kullback-Leibler divergence between the statistical parameters is used to estimate a distance between images. We demonstrated the effectiveness of the proposed descriptor for classification and retrieval tasks.