scholarly journals A cluster-based feature selection method for image texture classification

2019 ◽  
Vol 14 (3) ◽  
pp. 1433 ◽  
Author(s):  
Abbas F. H. Alharan ◽  
Hayder K. Fatlawi ◽  
Nabeel Salih Ali
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Clustering Algorithm ◽  
Gabor Filter ◽  
Texture Feature ◽  
Image Feature ◽  
Image Texture ◽  
Support Vector ◽  
Texture Image ◽  
Texture Feature Extraction

<p>Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.</p>

Intelligent sports feature recognition system based on texture feature extraction and SVM parameter selection

2020 ◽  
Vol 39 (4) ◽  
pp. 4847-4858
Author(s):  
Lei Wang ◽  
Jinhai Sun ◽  
Tuojian Li
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Extraction Method ◽  
Feature Recognition ◽  
Texture Feature ◽  
Texture Features ◽  
Spatial Domain ◽  
Image Texture ◽  
Support Vector ◽  
Texture Feature Extraction

Feature extraction is the basis of texture analysis. How to obtain texture features with small feature dimension, simple calculation and comprehensive representation of images is a hot spot and a difficult point in feature extraction. The traditional image texture feature extraction method is to process the image in the spatial domain. However, due to its high computational complexity, its practical application is restricted. Based on this, this study studies the extraction method of texture features, and deeply analyzes the principle of non-subsampled Contourlet transform. Moreover, this study uses NSCT to transform the image from the spatial domain to the frequency domain and extracts the texture features of the decomposed low frequency sub-band, intermediate frequency sub-band and high frequency sub-band image respectively. In addition, this study selects the appropriate parameters to establish the support vector machine model and applies the extracted texture features into the support vector machine for recognition and applies it to the sports feature recognition. Finally, this study designed a controlled experiment to analyze the performance of the algorithm. The results show that the proposed method has certain effects.

scholarly journals 3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors

2021 ◽  
Vol 11 (5) ◽  
pp. 2332
Author(s):  
Stefania Barburiceanu ◽  
Romulus Terebes ◽  
Serban Meza
Keyword(s):  
Feature Extraction ◽  
Imaging Techniques ◽  
Texture Feature ◽  
Local Binary Patterns ◽  
Image Feature ◽  
Support Vector ◽  
Volumetric Data ◽  
Texture Feature Extraction ◽  
Discrimination Power ◽  
Local Anisotropy

Lately, 3D imaging techniques have achieved a lot of progress due to recent developments in 3D sensor technologies. This leads to a great interest regarding 3D image feature extraction and classification techniques. As pointed out in literature, one of the most important and discriminative features in images is the textural content. Within this context, we propose a texture feature extraction technique for volumetric images with improved discrimination power. The method could be used in textured volumetric data classification tasks. To achieve this, we fuse two complementary pieces of information, feature vectors derived from Local Binary Patterns (LBP) and the Gray-Level Co-occurrence Matrix-based methods. They provide information regarding the image pattern and the contrast, homogeneity and local anisotropy in the volumetric data, respectively. The performance of the proposed technique was evaluated on a public dataset consisting of volumetric textured images affected by several transformations. The classifiers used are the Support Vector Machine, k-Nearest Neighbours and Random Forest. Our method outperforms other handcrafted 3D or 2D texture feature extraction methods and typical deep-learning networks. The proposed technique improves the discrimination power and achieves promising results even if the number of images per class is relatively small.

Improved LFP Algorithm on Leukocyte Image Texture Feature Extraction and Recognition

2013 ◽  
Vol 42 (11) ◽  
pp. 1375-1380 ◽  
Author(s):  
庞春颖 PANG Chunying ◽  
刘记奎 LIU Jikui
Keyword(s):  
Feature Extraction ◽  
Texture Feature ◽  
Image Texture ◽  
Texture Feature Extraction

scholarly journals Image Texture Feature Extraction Method Based on Regional Average Binary Gray Level Difference Co-occurrence Matrix

2011 ◽  
Vol 10 (3) ◽  
pp. 73-79 ◽  
Author(s):  
Jian Yang ◽  
Jingfeng Guo
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Texture Feature ◽  
Local Area ◽  
Image Texture ◽  
Gray Level ◽  
Texture Feature Extraction ◽  
Feature Extraction Method ◽  
Level Difference ◽  
Occurrence Matrix

Texture feature is a measure method about relationship among the pixels in local area, reflecting the changes of image space gray levels. This paper presents a texture feature extraction method based on regional average binary gray level difference co-occurrence matrix, which combined the texture structural analysis method with statistical method. Firstly, we calculate the average binary gray level difference of eight-neighbors of a pixel to get the average binary gray level difference image which expresses the variation pattern of the regional gray levels. Secondly, the regional co-occurrence matrix is constructed by using these average binary gray level differences. Finally, we extract the second-order statistic parameters reflecting the image texture feature from the regional co-occurrence matrix. Theoretical analysis and experimental results show that the image texture feature extraction method has certain accuracy and validity

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