In this paper, the authors propose a novel method for texture analysis to discriminate malignant GGO from benign GGO in LIDC/IDRI dataset. The proposed method for texture analysis is based on the oriented gray level co-occurrence matrix (GLCM), which is also proposed by the authors. The oriented GLCM has the advantages that the noise reduction is unnecessary, and arbitrary direction and distanc...

The features Gray Level Co-occurrence Matrix (GLCM) are mostly explored in Face Recognition and CBIR. GLCM technique is explored here for Copy-Move Forgery Detection. GLCMs are extracted from all the images in the database and statistics such as contrast, correlation, homogeneity and energy are derived. These statistics form the feature vector. Support Vector Machine (SVM) is trained on all the...

This paper presents an evaluation and comparison of the performance of three different feature extraction methods for classification of normal and abnormal patterns in mammogram. Three different feature extraction methods used here are intensity histogram, GLCM (Grey Level Co-occurrence Matrix) and intensity based features. A supervised classifier system based on neural network is used. The per...

The mission of automatically recognizing different facial expressions in human-computer environment is significant and challenging. This paper presents a method to identify the facial expressions by processing images taken from Facial Expression Database. The approach for emotion recognition is based on the texture features extracted from the gray-level co-occurrence matrix(GLCM) . The results ...

Abstract COVID-19 has caused over 6.35 million deaths and 555 confirmed cases till 11/July/2022. It a serious impact on individual health, social economic activities, other aspects. Based the gray-level co-occurrence matrix (GLCM), four-direction varying-distance GLCM (FDVD-GLCM) is presented. Afterward, five-property feature set (FPFS) extracts features from FDVD-GLCM. An extreme learning mach...

In this paper, an Artificial Neural Network (ANN) algorithm for classifying the EEG spectrogram images in brainwave is presented. Gray Level Co-occurrence Matrix (GLCM) texture feature from the EEG spectrogram images have been used as input to the system. The GLCM texture feature produced large dimension of feature, therefore the Principal Component Analysis (PCA) is used to reduce the feature ...

In this study, we proposed score fusion technique to improve the performance of remote sensing image retrieval system (RS-IRS) using combination of several features. The representation of each feature is selected based on their performance when used as single feature in RS-IRS. Those features are color moment using L*a*b* color space, edge direction histogram extracted from Saturation channel, ...

The purpose of the present text is to present the theory and techniques behind the Gray Level Coocurrence Matrix (GLCM) method, and the stateof-the-art of the field, as applied to two dimensional images. It does not present a survey of practical results. 1 Gray Level Coocurrence Matrices In statistical texture analysis, texture features are computed from the statistical distribution of observed...