Structural similarity-based video fingerprinting for video copy detection

The authors propose a video fingerprinting method based on structural similarity and a graph model. Structural similarity-based fingerprint generation and double-layer matching are the two main contributions. The video is mapped to a graph with frames as its vertices, and structural similarity is proposed to compute the weights of the edges. Then, the fingerprint consisting of a match tag (a coarse fingerprint) and a fine fingerprint is generated by this graph. The match tag is generated by an independent set of this graph, and the fine fingerprint is generated by the weight matrix of the graph based on the two-block-dimensional discrete cosine transform. During the matching, the video can be matched at the first-layer using the match tag to obtain a candidate set, whereas the second-layer matching is performed in this candidate set using the fine fingerprint to find a final match. The proposed video fingerprinting method is shown to be resistant to geometric attacks on frames and impairment of transmission channels.