A Gabor Wavelet Transformation-Based Texture Images Classification Algorithm

In this paper, we introduce a texture image classification algorithm based on Gabor wavelet transform. Using Gabor wavelet transform, image is decomposed into sub-bands images in multiresolution and multi-direction, and we extract texture feature from all sub-bands images. Then the algorithm groups feature image into clusters by the k near neighbor algorithm. The experimental results on dataset Brodatz showed that the proposed algorithm can achieve an ideal accuracy rate and excellent classification effect.

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Identification method of cashmere and wool based on texture features of GLCM and Gabor

Journal of Engineered Fibers and Fabrics ◽

10.1177/1558925021989179 ◽

2021 ◽

Vol 16 ◽

pp. 155892502198917

Author(s):

Yaolin Zhu ◽

Jiayi Huang ◽

Tong Wu ◽

Xueqin Ren

Keyword(s):

Wavelet Transform ◽

Frequency Domain ◽

Texture Feature ◽

Identification Accuracy ◽

Gabor Wavelet ◽

Gray Level ◽

Gabor Wavelet Transform ◽

Second Moment ◽

Feature Vectors ◽

Occurrence Matrix

The common texture feature extraction method is only in spatial or frequency domain, leading to insufficient texture information and low accuracy. The main aim of this paper is to present a novel texture feature analysis method based on gray level co-occurrence matrix and Gabor wavelet transform to sufficiently extract texture feature of cashmere and wool fibers. Firstly, the gray level co-occurrence matrix is constructed to calculate the four texture feature vectors including of contrast, angular second moment, dissimilarity and energy in spatial domain, and four texture feature vectors, which are contrast, angular second moment, mean and entropy, in frequency domain is obtained through Gabor wavelet transform and Gray-Scale difference statistics method. Then, because the contrast and angle second moment are used as descriptors of fiber image in both spatial and frequency domain, they are fused respectively by introducing a weight to make linear addition, making eight feature values compose a 6-dimensional feature vector. Finally, these feature vectors are fed into the Fisher classifier. The experimental results show that the identification accuracy of the proposed algorithm is improved by 0.682% compared to use 8-dimensional feature vectors describing the sample image. It verifies that the fused method based on texture feature in spatial and frequency domain is an effective approach to identify fibers of cashmere and wool.

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Aluminum Profile Type Recognition Based on Texture Features

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.2846 ◽

2014 ◽

Vol 556-562 ◽

pp. 2846-2851 ◽

Cited By ~ 1

Author(s):

Zhen Hua Liu ◽

Hai Bo Shi ◽

Xiao Feng Zhou

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Recognition Accuracy ◽

Detection Efficiency ◽

Texture Feature ◽

Gabor Wavelet ◽

Image Texture ◽

Support Vector ◽

Gabor Wavelet Transform ◽

Aluminum Profile

Because aluminum profile’s structure is complex and diverse, we need to match the different parameters for different profiles before automated detection of surface defects of aluminum profile. This process often requires manual input, affecting the detection efficiency. To solve this problem, we analyze the characteristics of aluminum profile, through GLCM algorithm and Gabor wavelet transform methods, which are image texture feature extraction methods to get aluminum profile’s texture feature, then we use the Support Vector Machine (SVM) classification algorithm based on radial basis function (RBF) core classify the feature, for the aim of matching parameters automatically. By feature extraction time and the recognition accuracy rate and other indicators to compare the experimental results of each method, derived using Gabor wavelet transform is the best both on recognition accuracy or identify time effects, and can satisfy the actual needs.

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