# Combined statistical and model based texture features for improved image classification

Source: [Combined statistical and model based texture features for improved image classification](<https://www.sota2.com/research/paper/68fa62441e209131ce9f85bf>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:16:31.033Z

Authors: Omar Al-Kadi

Published: 2015-12-29T23:28:13.000Z

## 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 naive 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1512.08814>)

[PDF](<https://arxiv.org/pdf/1512.08814.pdf>)