Action categorization with modified hidden conditional random field

Jianguo Zhang, Shaogang Gong

    Research output: Contribution to journalArticle

    71 Citations (Scopus)

    Abstract

    In this paper, we present a method for action categorization with a modified hidden conditional random field (HCRF). Specifically, effective silhouette-based action features are extracted using motion moments and spectrum of chain code. We formulate a modified HCRF (mHCRF) to have a guaranteed global optimum in the modelling of the temporal action dependencies after the HMM pathing stage. Experimental results on action categorization using this model are compared favorably against several existing model-based methods including GMM, SVM, Logistic Regression, HMM, CRF and HCRF.
    Original languageEnglish
    Pages (from-to)197-203
    Number of pages7
    JournalPattern Recognition
    Volume43
    Issue number1
    DOIs
    Publication statusPublished - 2010

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