The Comparison of Color Texture Features Extraction based on 1D GLCM with Deep Learning Methods
- 1 July 2020
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 285-289
Abstract
In this paper, the comparison between deep learning methods and feature extraction algorithms is presented. The principle of Grey-Level Co-occurrence Matrix (GLCM) and its modifications are used for our research. The main idea was to design a method for the description of combined features and textures. The texture classification process is carried out with the robust support vector machine classifier (SVM). We compare these feature extraction methods with proposed Convolutional Neural Networks (CNN). This proposed network contains 25 layers. Finally, the all evaluation and comparison of color texture retrieval results for all used methods are presented. The all feature extraction algorithms and proposed CNN have been tested on two different color texture datasets (Outex and Vistex datasets).Keywords
This publication has 24 references indexed in Scilit:
- Road Defect Classification Using Gray Level Co-Occurrence Matrix (GLCM) and Radial Basis Function (RBF)2018
- Content-based image retrieval by segmentation and clustering2017
- Tomatoes classification using K-NN based on GLCM and HSV color space2017
- An efficient content based image retrieval based on speeded up robust features (SURF) with optimization technique2017
- Color and texture fusion-based method for content-based Image Retrieval2017
- An Advanced Approach to Extraction of Colour Texture Features Based on GLCMInternational Journal of Advanced Robotic Systems, 2014
- Simple comparison of image segmentation algorithms based on evaluation criterion2011
- Outex - new framework for empirical evaluation of texture analysis algorithms2003
- Statistical and structural approaches to textureProceedings of the IEEE, 1979
- Textural Features for Image ClassificationIEEE Transactions on Systems, Man, and Cybernetics, 1973