Improved LBP texture classification using ensemble learning

Texture analysis and classification play an important role in many multimedia and computer vision applications. Local binary patterns (LBP) form a simple yet powerful texture descriptor characterising local neighbourhood properties, and consequently LBP variants are widely employed. In this paper, we demonstrate that through appropriate construction of a multiple classifier system, improved texture classification based on LBP features is possible. In particular, we employ a classifier ensemble where each classifier (a support vector machine) is trained in conjunction with a different feature selection method. The ensemble is then pruned based on a diversity measure, and the remaining models are combined using a neural fuser. Experimental results, obtained on Outex benchmark datasets and employing four LBP variants, confirm that our proposed approach leads to statistically significantly improved texture classification.