For large scale automatic semantic video characterization, it is necessary to learn and model a large number of semantic concepts. But a major obstacle to this is the insufficiency of labeled training samples. Multi-view semi-supervised learning algorithms such as co-training may help by incorporating a large amount of unlabeled data. However, one of their assumptions requiring that each view be sufficient for learning is usually violated in semantic concept detection. In this paper, we propose a novel multi-view semi-supervised learning algorithm called semi-supervised cross feature learning (SCFL). The proposed algorithm has two advantages over co-training. First, SCFL can theoretically guarantee its performance not being significantly degraded even when the assumption of view sufficiency fails. Also, SCFL can also handle additional views of unlabeled data even when these views are absent from the training data. As demonstrated in the TRECVID '03 semantic concept extraction task, the proposed SCFL algorithm not only significantly outperforms the conventional co-training algorithms, but also comes close to achieving the performance when the unlabeled set were to be manually annotated and used for training along with the labeled data set.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Semi-supervised cross feature learning for semantic concept detection in videos


    Beteiligte:
    Rong Yan, (Autor:in) / Naphade, M. (Autor:in)


    Erscheinungsdatum :

    2005-01-01


    Format / Umfang :

    192330 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Fatigue driving detection model based on multi‐feature fusion and semi‐supervised active learning

    Li, Xu / Hong, Lin / Wang, Jian‐chun et al. | Wiley | 2019

    Freier Zugriff

    Fatigue driving detection model based on multi-feature fusion and semi-supervised active learning

    Li, Xu / Hong, Lin / Wang, Jian-chun et al. | IET | 2019

    Freier Zugriff

    Semi-supervised learning and feature evaluation for RGB-D object recognition

    Cheng, Y. / Zhao, X. / Huang, K. et al. | British Library Online Contents | 2015



    Pilot Hand Detection Based on Semi-Supervised Learning

    Qian, Ying / Gu, Renshu / Gu, Hongbin | IEEE | 2022