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

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

Enkelte spørsmål om merverdiavgift ved eksport av varer til Norge

Textures show multi-scale properties and hence multiresolution techniques are considered appropriate for texture classification. Recently, the authors proposed a multiresolution texture classification system based on scale space theory and combined classifiers. However, the use of multiresolution techniques increases the computational load and memory space required. Sub-sampling can help to reduce these side effects of multiresolution techniques. However, it may degrade the overall performance of the classification system. In this paper the effect of sub-sampling is investigated in scale space texture classification using combined classifiers. It is shown that sub-sampling can help to reduce both computational load and memory space required without compromising the performance of the system