Automatic Unsupervised Texture Recognition Framework Using Anisotropic Diffusion-Based Multi-Scale Analysis and Weight-Connected Graph Clustering

A novel unsupervised texture classification technique is proposed in this research work. The proposed method clusters automatically the textures of an image collection in similarity classes whose number is not a priori known. A nonlinear diffusion-based multi-scale texture analysis approach is intro...

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Bibliographic Details
Published in:Symmetry (Basel) Vol. 13; no. 6; p. 925
Main Author: Barbu, Tudor
Format: Journal Article
Language:English
Published: Basel MDPI AG 23.05.2021
Subjects:
Algorithms
Apexes
Approximation
Balances (scales)
Classification
Clustering
Computation
Diffusion
Feature extraction
Graph theory
Mathematical models
Multiscale analysis
Partial differential equations
Similarity
Texture recognition
Wavelet transforms
ISSN:2073-8994, 2073-8994
Online Access:Get full text
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Summary:A novel unsupervised texture classification technique is proposed in this research work. The proposed method clusters automatically the textures of an image collection in similarity classes whose number is not a priori known. A nonlinear diffusion-based multi-scale texture analysis approach is introduced first. It creates an effective scale-space by using a well-posed anisotropic diffusion filtering model that is proposed and approximated numerically here. A feature extraction process using a bank of circularly symmetric 2D filters is applied at each scale, then a rotation-invariant texture feature vector is achieved for the current image by combining the feature vectors computed at all these scales. Next, a weighted similarity graph, whose vertices correspond to the texture feature vectors and the weights of its edges are obtained from the distances computed between these vectors, is created. A novel weighted graph clustering technique is then applied to this similarity graph, to determine the texture classes. Numerical simulations and method comparisons illustrating the effectiveness of the described framework are also discussed in this work.
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ISSN:2073-8994
2073-8994
DOI:10.3390/sym13060925