Image Signature Robust to Caption Superimposition for Video Sequence Identification

This paper proposes an image signature robust to caption superimposition for video sequence identification. A new image signature which is a set of local features is developed for a high-speed frame-by-frame matching of video sequences. The signature of a frame is obtained by partitioning the image into blocks and extracting the local feature representing the dominant type of edge direction from each block. The similarity between the signatures is calculated by comparing the edge types of the corresponding blocks, and counting the number of the blocks having the same edge type. A weighting scheme based on the probability of caption superimposition for each block can be applied to the similarity calculation to improve the matching performance. The experimental results of the video sequence identification show that the proposed signature achieves precision of 99.65% and recall of 99.45%, improving both the precision and the recall by more than 30% compared with the conventional signature.