Combining frequent 2-itemsets and statistical features for texture classification in wavelet domain

2013 ◽  
Author(s):  
Li Liu ◽  
Haojie Wang ◽  
Meijiao Wang ◽  
Cheng Zhang
Keyword(s):  
Texture Classification ◽  
Wavelet Domain ◽  
Statistical Features

scholarly journals Texture Classification Using Scattering Statistical and Cooccurrence Features

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Juan Wang ◽  
Jiangshe Zhang ◽  
Jie Zhao
Keyword(s):  
Image Processing ◽  
Wavelet Transforms ◽  
Texture Classification ◽  
Texture Features ◽  
Research Topic ◽  
Experimental Results ◽  
Statistical Features ◽  
Important Research ◽  
Scattering Transform ◽  
Important Research Topic

Texture classification is an important research topic in image processing. In 2012, scattering transform computed by iterating over successive wavelet transforms and modulus operators was introduced. This paper presents new approaches for texture features extraction using scattering transform. Scattering statistical features and scattering cooccurrence features are derived from subbands of the scattering decomposition and original images. And these features are used for classification for the four datasets containing 20, 30, 112, and 129 texture images, respectively. Experimental results show that our approaches have the promising results in classification.

Effective texture classification by texton encoding induced statistical features

2015 ◽  
Vol 48 (2) ◽  
pp. 447-457 ◽  
Author(s):  
Jin Xie ◽  
Lei Zhang ◽  
Jane You ◽  
Simon Shiu
Keyword(s):  
Texture Classification ◽  
Statistical Features

scholarly journals CLASSIFICATION OF TEXTURES WITH AND WITHOUT ROTATION ANGLES: A DAUBECHIES WAVELET BASED APPROACH

2014 ◽  
pp. 49-54
Author(s):  
SHAIKHJI ZAID M ◽  
J B JADHAV ◽  
V N KAPADIA
Keyword(s):  
Image Processing ◽  
Texture Classification ◽  
Daubechies Wavelet ◽  
Statistical Features ◽  
Visual Texture ◽  
Multi Resolution Analysis ◽  
Real Objects ◽  
Resolution Analysis ◽  
Definition Of

Textures play important roles in many image processing applications, since images of real objects often do not exhibit regions of uniform and smooth intensities, but variations of intensities with certain repeated structures or patterns, referred to as visual texture. The textural patterns or structures mainly result from the physical surface properties, such as roughness or oriented structured of a tactile quality. It is widely recognized that a visual texture, which can easily perceive, is very difficult to define. The difficulty results mainly from the fact that different people can define textures in applications dependent ways or with different perceptual motivations, and they are not generally agreed upon single definition of texture [1]. The development in multi-resolution analysis such as Gabor and wavelet transform help to overcome this difficulty. In this paper it describes that, texture classification using Wavelet Statistical Features (WSF), Wavelet Co-occurrence Features (WCF) and a combination of wavelet statistical features and co-occurrence features of wavelet transformed images with different feature databases can results better [2]. Several Image degrading parameters are introduced in the image to be classified for verifying the features. Wavelet based decomposing is used to classify the image with code prepared in MATLAB.

Performance Analysis of Anisotropic Diffusion Based Colour Texture Descriptors in Industrial Applications

2017 ◽  
Vol 7 (2) ◽  
pp. 50-63 ◽  
Author(s):  
Prakash S. Hiremath ◽  
Rohini A. Bhusnurmath
Keyword(s):  
Performance Analysis ◽  
Anisotropic Diffusion ◽  
State Of The Art ◽  
Texture Classification ◽  
Industrial Applications ◽  
Statistical Features ◽  
Linear Discriminant ◽  
Class Separability ◽  
Novel Method

A novel method of colour texture analysis based on anisotropic diffusion for industrial applications is proposed and the performance analysis of colour texture descriptors is examined. The objective of the study is to explore different colour spaces for their suitability in automatic classification of certain textures in industrial applications, namely, granite tiles and wood textures, using computer vision. The directional subbands of digital image of material samples obtained using wavelet transform are subjected to anisotropic diffusion to obtain the texture components. Further, statistical features are extracted from the texture components. The linear discriminant analysis is employed to achieve class separability. The texture descriptors are evaluated on RGB, HSV, YCbCr, Lab colour spaces and compared with gray scale texture descriptors. The k-NN classifier is used for texture classification. For the experimentation, benchmark databases, namely, MondialMarmi and Parquet are considered. The experimental results are encouraging as compared to the state-of-the-art-methods.

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