Automatic identification of digital video based on shot-level sequence matching

To locate a video clip in large collections is very important for retrieval applications, especially for digital rights management. In this paper, we present a novel technique for automatic identification of digital video. This new algorithm is based on dynamic programming that fully uses the temporal dimension to measure the similarity between two video sequences. A normalized chromaticity histogram is used as a feature which is illumination-invariant. Dynamic programming is applied on shotlevel to find the optimal nonlinear mapping between video sequences. Two new normalized distance measures are presented for video sequence matching. One measure is based on the normalization of the optimal path found by dynamic programming. The other measure combines both the visual features and the temporal information. Experimental results show that the shot-level approach is robust to frame rate conversion, color correction, and compressions. The proposed distance measures are suitable for varia...

• Type: Conference
• Year: 2005
• Where: MM
• Authors: Jian Zhou, Xiao-Ping Zhang
• Keywords: Dynamic Programming, MM 2005, Video Sequence, Video Sequence Matching