• [Show abstract] [Hide abstract]
    ABSTRACT: Content-based copy detection (CBCD) recently has appeared a promising technique for video monitoring and copyright protection. In this paper, a novel framework for CBCD is proposed. Robust global features and local Speeded Up Robust Features (SURF) are first combined to describe video contents, and the density sampling method is proposed to improve the generation of visual codebook. Secondly, Smith-Waterman algorithm is introduced to find the similar video segments, meanwhile, a video matching method based on visual codebook is proposed to calculate the similarity of copy videos. Finally, a hierarchical fusion scheme is used to refine the detection results. Experiments on TRECVID dataset show that the proposed framework gives better results than the average results of CBCD task in TRECVID 2008.
    01/2010;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Searching of video clip from TV streams has significant commercial values, for example, the application of advertisement tracking in different TV channels. In this paper, a segment-based advertisement searching method is proposed. Robust visual features are first extracted to overcome some video transformations, and then two search strategies are presented for long AD clips and short ones. The experimental results indicate that the average search time of one query clip from a 24-hour TV stream is about 1 second, and the mean recall for 9 channels exceeds 98% while 100% precision is achieved. The evaluation results of copy detection task at TRECVID show the robustness and effectiveness.
    01/2010;
  • [Show abstract] [Hide abstract]
    ABSTRACT: The automatic recording function of DVR is a powerful tool for users. However, increase of the stored content makes it difficult to access desired content. To solve this issue, this paper proposes a new method of providing suitable thumbnails of TV programs by detecting important objects from them. Our approach is based on identifying typical shooting and editing techniques, which are estimated from camera motion and visual features density. The proposed method is independent of types of target object and it achieves detection accuracy of about 79%, which outperforms the existing object-dependent approaches. The method is applied to the prototype application on the DVR. It enables the user to find desired content intuitively and access important scenes easily.
    IEEE Transactions on Consumer Electronics 06/2010; · 1.09 Impact Factor

Full-text

Download
0 Downloads
Available from