Combined statistical and model based texture features for improved image classification

Al-Kadi, O.S. (2008) Combined statistical and model based texture features for improved image classification. In: 4th International Conference on Advances in Medical, Signal & Information Processing, 14 - 16 JULY 2008, Santa Margherita Ligrue, Italy.

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Abstract

This paper aims to improve the accuracy of texture classification based on extracting texture features using five different texture methods and classifying the patterns using a naïve Bayesian classifier. Three statistical-based and two model-based methods are used to extract texture features from eight different texture images, then their accuracy is ranked after using each method individually and in pairs. The accuracy improved up to 97.01% when model based – Gaussian Markov random field (GMRF) and fractional Brownian motion (fBm) – were used together for classification as compared to the highest achieved using each of the five different methods alone; and proved to be better in classifying as compared to statistical methods. Also, using GMRF with statistical based methods, such as Gray level co-occurrence (GLCM) and run-length (RLM) matrices, improved the overall accuracy to 96.94% and 96.55%; respectively.

Item Type: Conference or Workshop Item (Paper)
Keywords: Image classification, texture analysis, Bayesian classifier
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Q Science > QA Mathematics > QA0076 Computer software
Depositing User: Omar Al-Kadi
Date Deposited: 30 Sep 2008
Last Modified: 30 Nov 2012 16:53
URI: http://sro.sussex.ac.uk/id/eprint/1891
Google Scholar:3 Citations

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