Robust Boosted Parameter Based Combined Classifier for Rotation Invariant Texture Classification

Texture analysis and classification remain as one of the biggest challenges for the field of computer vision and pattern recognition. This article presents a robust hybrid combination technique to build a combined classifier that is able to tackle the problem of classification of rotation-invariant 2D textures. Diversity in the components of the combined classifier is enforced through variation of the parameters related to both architecture design and training stages of a neural network classifier. The boosting algorithm is used to make perturbation of the training set using Multi-Layer Perceptron (MLP) as the base classifier. The final decision of the proposed combined classifier is based on the majority voting. Experiments' results on a standard benchmark database of rotated textures show that the proposed hybrid combination method is very robust, and it presents an excellent texture discrimination for all considered classes, overcoming traditional texture modification methods.