Journal ArticleDOI
Texture classification using wavelet transform
S. Arivazhagan,L. Ganesan +1 more
TLDR
This paper describes the texture classification using (i) wavelet statistical features, (ii) wavelets co-occurrence features and (iii) a combination of wavelets statistical features and co- Occurrence features of one level wavelet transformed images with different feature databases.About:
This article is published in Pattern Recognition Letters.The article was published on 2003-06-01. It has received 522 citations till now. The article focuses on the topics: Discrete wavelet transform & Wavelet transform.read more
Citations
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Journal ArticleDOI
Texture classification using Gabor wavelets based rotation invariant features
TL;DR: This paper presents a new approach for rotation invariant texture classification using Gabor wavelets, which has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain.
Proceedings Article
A Review on Image Feature Extraction and Representation Techniques
TL;DR: This paper analyzes the effectiveness of the fusion of global and local features in automatic image annotation and content based image retrieval community, including some classic models and their illustrations in the literature.
Journal ArticleDOI
Wavelet-Based Energy Features for Glaucomatous Image Classification
TL;DR: This paper proposes a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies, achieving an accuracy of around 93% using tenfold cross validations.
Journal ArticleDOI
Texture segmentation using wavelet transform
S. Arivazhagan,L. Ganesan +1 more
TL;DR: The performance of this segmentation algorithm is superior to traditional single resolution techniques such as texture spectrum, co-occurrences, local linear transforms, etc.
Journal ArticleDOI
Content-based image classification using a neural network
TL;DR: A method of content-based image classification using a neural network for shape-based texture features extracted from wavelet-transformed images and among the various texture features, the diagonal moment was the most effective.
References
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Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI
Texture features for browsing and retrieval of image data
B.S. Manjunath,Wei-Ying Ma +1 more
TL;DR: Comparisons with other multiresolution texture features using the Brodatz texture database indicate that the Gabor features provide the best pattern retrieval accuracy.
Journal ArticleDOI
Multichannel texture analysis using localized spatial filters
TL;DR: An interpretation of image texture as a region code, or carrier of region information, is emphasized and examples are given of both types of texture processing using a variety of real and synthetic textures.
A comparative study of texture measures for terrain classification.
J. S. Weszka,A. Rosenfeld +1 more
TL;DR: Three standard approaches to automatic texture classification make use of features based on the Fourier power spectrum, on second-order gray level statistics, and on first-order statistics of gray level differences, respectively; it was found that the Fouriers generally performed more poorly, while the other feature sets all performned comparably.
Journal ArticleDOI
Texture analysis and classification with tree-structured wavelet transform
T. Chang,C.-C.J. Kuo +1 more
TL;DR: A progressive texture classification algorithm which is not only computationally attractive but also has excellent performance is developed and is compared with that of several other methods.