Comparative study of image texture classification techniques

Author

Dash, Sonali ; Chiranjeevi, K. ; Jena, U.R. ; Trinadh, Akula

Author_Institution

Dept of ECE, VSSUT, Burla, Sambalpur, Odisha, India

fYear

2015

fDate

24-25 Jan. 2015

Firstpage

1

Lastpage

6

Abstract

This paper contains study and review of various techniques used for feature extraction and texture classification. The objective of study is to find technique or combination of techniques to reduce complexity, speed while increasing the accuracy at the same time. Here we are studying and reviewing the three feature extraction methods: Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter method. Also classification methods like KNN, SVM, evolving fuzzy neural network (efunn), genetic algorithm and higher-order statistics. These are used on the texture datasets Brodatz, CUReT, VisTex and OuTex for the experimental purpose. In this paper, we present a comparative study of image texture classification techniques which are very much help full for image classifications

Keywords

Accuracy; Databases; Feature extraction; Gabor filters; Sociology; Statistics; Support vector machines; Feature Extraction; Texture classification; evolving fuzzy neural network (EFuNN); genetic algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical, Electronics, Signals, Communication and Optimization (EESCO), 2015 International Conference on

Conference_Location

Visakhapatnam, India

Print_ISBN

978-1-4799-7676-8

Type

conf

DOI

10.1109/EESCO.2015.7253732

Filename

7253732