Texture classification using random forests and support vector machines

Mohammed M. Al Samarraie, Md. Jan Nordin, Ghassan Jasim Al-Anizy

Research output: Contribution to journalArticle

Abstract

Texture analysis is considered fundamental and important in the fields of pattern recognition, computer vision and image processing. Texture analysis mainly aims to computationally represent an intuitive perception of texture and to facilitate automatic processing of the texture information for artificial vision systems. In this paper, we have compared between texture classification methods based on the Random Forest (RF) and Support Vector Machine (SVM) classifiers by using various extraction feature methods namely bi-orthogonal wavelet transform, gray level histogram and co-occurrence matrices. Each of these methods has used to classify the image separately at first, and they have combined together secondly. Experiments were conducted on two different databases. The first texture database is CUReT and the second database was collected from Outex database. The results have revealed that, RF and SVM have yielded higher classification precision.

Original languageEnglish
Pages (from-to)232-238
Number of pages7
JournalJournal of Theoretical and Applied Information Technology
Volume73
Issue number2
Publication statusPublished - 2015

Fingerprint

Texture Classification
Random Forest
Support vector machines
Support Vector Machine
Textures
Texture
Texture Analysis
Co-occurrence Matrix
Biorthogonal Wavelets
Computer vision
Vision System
Computer Vision
Histogram
Wavelet Transform
Feature Extraction
Pattern Recognition
Intuitive
Image Processing
Classify
Classifier

Keywords

  • Feature extraction
  • Supervised random forest
  • Support vector machine
  • Texture classification

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Texture classification using random forests and support vector machines. / Al Samarraie, Mohammed M.; Nordin, Md. Jan; Jasim Al-Anizy, Ghassan.

In: Journal of Theoretical and Applied Information Technology, Vol. 73, No. 2, 2015, p. 232-238.

Research output: Contribution to journalArticle

Al Samarraie, Mohammed M. ; Nordin, Md. Jan ; Jasim Al-Anizy, Ghassan. / Texture classification using random forests and support vector machines. In: Journal of Theoretical and Applied Information Technology. 2015 ; Vol. 73, No. 2. pp. 232-238.
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