Scale-Space Texture Classification Using Combined Classifiers

Since texture is scale dependent, multi-scale techniques are quite useful for texture classification. Scale-space theory introduces multi-scale differential operators. In this paper, the N-jet of derivatives up to the second order at different scales is calculated for the textures in Brodatz album to generate the textures in multiple scales. After some preprocessing and feature extraction using principal component analysis (PCA), instead of combining features obtained from different scales/derivatives to construct a combined feature space, the features are fed into a two-stage combined classifier for classification. The learning curves are used to evaluate the performance of the proposed texture classification system. The results show that this new approach can significantly improve the performance of the classification especially for small training set size. Further, comparison between combined feature space and combined classifiers shows the superiority of the latter in terms of performance and computation complexity.