Comparison of support vector machines with autocorrelation kernels for invariant texture classification

2004 ◽  
Author(s):  
Y. Horikawa
Keyword(s):  
Support Vector Machines ◽  
Texture Classification ◽  
Support Vector ◽  
Vector Machines

Texture classification using the support vector machines

2003 ◽  
Vol 36 (12) ◽  
pp. 2883-2893 ◽  
Author(s):  
Shutao Li ◽  
James T. Kwok ◽  
Hailong Zhu ◽  
Yaonan Wang
Keyword(s):  
Support Vector Machines ◽  
Texture Classification ◽  
Support Vector ◽  
Vector Machines

scholarly journals Optimal filter bank representation in texture classification using support vector machines

2021 ◽  
Author(s):  
Samsher Singh Sidhu
Keyword(s):  
Support Vector Machines ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Texture Classification ◽  
Classification Method ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Translation Invariant ◽  
Vector Machines ◽  
Wavelet Frame Transform

Texture analysis has been a field of study for over three decades in many fields including electrical engineering. Today, texture analysis plays a crucial role in many tasks ranging from remote sensing to medical imaging. Researchers in this field have dealt with many different approaches, all trying to achieve the goal of high classification accuracy. The main difficulty of texture analysis was the lack of ability of the tools to characterize adequately different scales of the textures effectively. The development in multi-resolution analysis such as Gabor and Wavelet Transform help to overcome this difficulty. This thesis describes the texture classification algorithm that uses the combination of statistical features and co-occurrence features of the Discrete Wavelet Transformed images. The classification accuracy is increased by using translation-invariant features generated from the Discrete Wavelet Frame Transform. The results are further improved by focussing on the transformed images used for feature extraction by using filters which essentially extract those areas of the image that discriminate themselves from other image classes. In effect, by reducing the spatial characteristics of images that contribute to the features, the texture classification method still has the ability to preserve the classification accuracy. Support Vector Machines has proved excellent performance in the area of pattern recognition problems. We have applied SVMs with the texture classification method described above and, when compared to traditional classifiers, SVM has produced more accurate classification results on the Brodatz texture album.

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