A multi-modal information fusion technique integrating the closed caption, anchor's speech, and visual information for TV news video classification is presented. By recognizing closed-caption characters from video, phrases of single- and double-character are found for classification. On the other hand, content of the anchor's speech signal is not recognized, but instead, labeled with pre-trained cluster means by using a level-building DP (dynamic programming) algorithm. Visual information, including the color and motion features, is extracted from the news footage part for classification. The above three information is individually classified by using statistical relevance factor (RF) or SVM (support vector machine) technique, amounting to 7 different classifiers. Results of multiple classifiers are then combined to get fused outputs by using a modified Bayesian technique. Experiments show that the proposed fusion system is capable of increasing the classification rate by 14% with respect to the best single-modal system. Our Bayesian fusion rule also outperforms the best product rule presented in J. Kittler, et al (1998) by 3%.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    News video classification based on multi-modal information fusion


    Beteiligte:
    Wen-Nung Lie, (Autor:in) / Chen-Kang Su, (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    271164 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    News Video Classification Based on Multi-modal Information Fusion

    Lie, W.-N. / Su, C.-K. | British Library Conference Proceedings | 2005


    Multi-modal fusion for video understanding

    Hoogs, A. / Mundy, J. / Cross, G. | IEEE | 2001


    Multi-Modal Fusion for Video Understanding

    Hoogs, A. / Mundy, J. / Cross, G. | British Library Conference Proceedings | 2001


    Multi-Modal Fusion for Enhanced Automatic Modulation Classification

    Li, Yingkai / Wang, Shufei / Zhang, Yibin et al. | IEEE | 2024


    Multi-Modal Fusion Technology Based on Vehicle Information: A Survey

    Zhang, Xinyu / Gong, Yan / Lu, Jianli et al. | IEEE | 2023