This article proposes an integrated system for segmentation and classification of two moving objects, including car and pedestrian from their side-view in a video sequence. Based on the use of grey-level co-occurrence matrix (GLCM) in Haar wavelet transformed space, the authors calculated features of texture data from different sub-bands separately. Haar wavelet transform is chosen because the resulting wavelet sub-bands are strongly affecting on the orientation elements in the GLCM computation. To evaluate the proposed method, the results of different sub-bands are compared with each other. Extracted features of objects are classified by using a support vector machine (SVM). Finally, the experimental results showed that use of three sub-bands of wavelets instead of two sub-bands is more effective and has good precision.


    Access

    Access via TIB


    Export, share and cite



    Title :

    Using GLCM features in Haar wavelet transformed space for moving object classification


    Contributors:

    Published in:

    Publication date :

    2019-04-05


    Size :

    6 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Using GLCM features in Haar wavelet transformed space for moving object classification

    Kiaee, Nadia / Hashemizadeh, Elham / Zarrinpanjeh, Nima | Wiley | 2019

    Free access


    Investigation of Image Classification Using HOG, GLCM Features, and SVM Classifier

    Ge, Jianyue / Liu, Haoting | British Library Conference Proceedings | 2020



    Segmentation of Object Surfaces using the Haar Wavelet at Multiple Resolutions

    Miller, J. T. / Li, C. C. / IEEE; Signal Processing Society | British Library Conference Proceedings | 1994