Parameter estimation is a key computational issue in all statistical image modeling techniques. In this paper, we explore a computationally efficient parameter estimation algorithm for multi-dimensional hidden Markov models. 2-D HMM has been applied to supervised aerial image classification and comparisons have been made with the first proposed estimation algorithm. An extensive parametric study has been performed with 3-D HMM and the scalability of the estimation algorithm has been discussed. Results show the great applicability of the explored algorithm to multi-dimensional HMM based image modeling applications.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Parameter estimation of multi-dimensional hidden Markov models - a scalable approach


    Contributors:
    Joshi, D. (author) / Jia Li, (author) / Wang, J.Z. (author)


    Publication date :

    2005-01-01


    Size :

    271343 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Parameter Estimation of Multi-Dimensional Hidden Markov Models - A Scalable Approach

    Joshi, D. / Li, J. / Wang, J. Z. | British Library Conference Proceedings | 2005


    A Self-Adaptive Parameter Selection Trajectory Prediction Approach via Hidden Markov Models

    Qiao, Shaojie / Shen, Dayong / Wang, Xiaoteng et al. | IEEE | 2015



    HIDDEN MARKOV MODEL PARAMETER ESTIMATION FOR MULTIPLE DIM TARGET DETECTION

    Shim, S.-W. / Won, D.-Y. / Tahk, M.-J. et al. | British Library Conference Proceedings | 2012


    Parameter Estimation in Hidden Fuzzy Markov Random Fields and Image Segmentation

    Salzenstein, F. / Pieczynski, W. | British Library Online Contents | 1997