A new class of features for wavelet-based texture classification is introduced using a new feature-weighting scheme adapted to non-Euclidean similarity measures. The feature extraction is based on the histogram of the local second moment estimates of the wavelet transform. It is shown that the bins' centers of such histograms should be scaled logarithmically rather than linearly. The distance between two texture features is measured using the x/sup 2/ similarity measure, weighted according to the feature's degree of dispersion within the training dataset. Classification experiments of the proposed approach using an orthonormal wavelet transform show improved classification results compared to presently available methods.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A new approach to feature extraction for wavelet-based texture classification


    Beteiligte:
    Mittelman, R. (Autor:in) / Porat, M. (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    153682 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Neural Network-Based Color Texture Classification with Wavelet Packets for Feature Extraction

    Rani, B. S. / Renganathan, S. | British Library Online Contents | 2004


    A New Approach to Feature Exatraction for Wavelet-Based Texture Classification

    Mittelman, R. / Porat, M. | British Library Conference Proceedings | 2005


    Texture classification based on Markov modeling in wavelet feature space

    Shirazi, M. N. / Noda, H. / Takao, N. | British Library Online Contents | 2000