A cluster-based feature selection method for image texture classification

Abstract

<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>

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

“…Alharan et al [63] proposed a method based on feature extraction and feature selection methods for texture image classification. Firstly, the set of features was extracted from the used datasets by using three approaches (Gray Level Cooccurrence Matrix (GLCM), Local Binary Pattern (LBP), and Gabor filter).…”

“…In the proposed feature selection methods, different techniques/algorithms used to get the dimensionality reduction of the dataset, minimize computation time, and improve classification accuracy. Through the literature and table III there are three methods ( [51,58,63]) dependent on the clustering technique using K-means. The authors in [51] used K-means for removing non-relevant features, while [58] in the similarity value was used to separate the features in multiple clusters, and in [44] the algorithm was used to divide the features into the most relevant and noisy clusters.…”

A survey on local binary pattern and gabor filter as texture descriptors of smart profiling systems

“…As shown in Figure 4, most of the studies that have examined the combination of Gabor and LBP were intended to extract more local and global features in order to overcome the specific issue. [31] Gabor + LBP + LPQ Blur (low-resolution) Face recognition Hadizadeh [32] Gabor + LBP Extending local and global features Texture classification Tao et al [33] Gabor + LBP Extending local and global features Face recognition Huang et al [34] Gabor + LBP + PCA Extending local and global features Object recognition Liu et al [35] 2D Gabor + LBP Extending local and global features Face recognition Khaleefah et al [36] Gabor + LBP Parameter tuning Paper fingerprinting Alharan et al [37] Gabor…”

Online geocode in postal address using GPS with synchronous database accessing

“…In the internet era, huge data are produced, manipulate, and, consume and it was considered "one of the essential value-added pieces of information" among the advancement of the internet-based services [1][2][3]. Hence, maps and location-based services and applications are spread via widespread adoption recently [4][5][6].…”