A novel information theoretic approach to wavelet feature selection for texture classification
MetadataShow full item record
In this research we address the problem of discriminant subband selection for texture classification. A novel Effective Information based Subband Selection (EISS) algorithm is proposed which utilizes the intra-class and inter-class distributions. Essentially these distributions are used to calculate the class-based entropy for a given subband. This class-based information is incorporated in the total information content of the training images to develop a robust Effective Information (EI) criterion. Only the subbands with the top EI criteria are allowed to participate in the classification process. The proposed EISS algorithm is evaluated on Brodatz texture database and has shown to outperform the most relevant method based on mutual information criterion.
Showing items related by title, author, creator and subject.
Stachowiak, G.P.; Stachowiak, Gwidon; Podsiadlo, Pawel (2008)In this study, the automated classification system, developed previously by the authors, was used to classify wear particles. Three kinds of wear particles, fatigue, abrasive and adhesive, were classified. The fatigue ...
Fractals and fuzzy sets for modelling the heterogenity and spatial complexity of urban landscapes using multiscale remote sensing dataIslam, Zahurul (2004)This research presents models for the analysis of textural and contextual information content of multiscale remote sensing to select an appropriate scale for the correct interpretation and mapping of heterogeneous urban ...
Potential evaluation of different types of images and their combination for the classification of GIS objects cropland and GrasslandRecio, J.; Helmholz, Petra; Müller, S. (2012)In many publications the performance of different classification algorithms regarding to agricultural classes is evaluated. In contrast, this paper focuses on the potential of different imagery for the classification of ...