Combined Classifier versus Combined Feature Space in Scale Space Texture Classification

Using combined classifiers alleviates the problem of generating a large feature space, as the features generated from each scale/derivative are directly fed to a base classifier. In this approach, instead of concatenating features generated from each scale/derivative, the decision made by the base classifiers are combined in a two-stage combined classifier.In this paper, the performance of the proposed classification system is first compared against the combined feature space for only the zeroth order Gaussian derivative at multiple scales. The results clearly show that the proposed system using combined classifiers outperforms the classical approach of the combined feature space. The significance of the parameters, especially the fraction of variance maintained after applying PCA (principal component analysis) is also discussed.