Video Similarity Measurement Based on Attributed Relational Graph Matching

In this paper, an original scheme for video similarity detection is proposed in order to establish correspondence between two video sequences. This scheme consists first to summarize the visual contents of a video sequence in a small set of images. Each image is then modeled, by an Attributed Relational Graph (ARG), as the composition of salient objects with specific spatial relationship. Matching two video sequences is thereby reduced to the ARG similarity problem. The proposed approach offers a principled way to define the ARG similarity that accounts for both the attribute and topological differences of the two considered ARGs. Indeed, we proposed herein a cost-efficient solution to find the best alignment between two ARGs. This consists to the minimization of a similarity measure between the two graphs using dynamic programming. This measure can be considered as a matching rate which can be very useful for Content Based Video Retrieval (CBVR) applications. The suggested scheme was preliminary tested on real-world databases and very promising results were observed.