The problem of using noncausal hidden Markov models (HMMs) for texture classification is addressed. In noncausal models, the state of each pixel may be dependent on its neighbors in all directions. New algorithms are given to learn the parameters of a noncausal HMM of a texture and to classify it into one of several learned categories. The efficacy of these algorithms for texture classification is determined by classification experiments involving both synthetically generated and natural textures. A comparison to recent results in autocorrelation modeling demonstrates that similar classification accuracy can be achieved using noncausal HMMs that learn fewer parameters.<>


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Texture classification using noncasual hidden Markov models


    Contributors:
    Povlow, B.R. (author) / Dunn, S.M. (author)


    Publication date :

    1993-01-01


    Size :

    169661 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Hyperspectral Texture Classification Using Generalized Markov Fields

    Sarkar, S. / Healey, G. / IEEE Computer Society | British Library Conference Proceedings | 2004




    Color Texture Retrieval through Contourlet-Based Hidden Markov Model

    He, Z. / Bystrom, M. | British Library Conference Proceedings | 2005