A heuristic for the retrieval of objects in video in the framework of the rough indexing paradigm
Abstract
In this paper, we tackle the problem of matching of objects in video in the framework of the rough indexing paradigm. In this context, the video data are of very low spatial and temporal resolution because they come from partially decoded MPEG compressed streams. This paradigm enables us to achieve our purpose in near real time due to the faster computation on rough data than on original full spatial and temporal resolution video frames. In this context, segmentation of rough video frames is inaccurate and the region features (texture, color, shape) are not strongly relevant. The structure of the objects must be considered in order to improve the robustness of the matching of regions. The problem of object matching can be expressed in terms of region adjacency graph (RAG) matching. Here, we propose a directed acyclic graph (DAG) matching method based on a heuristic in order to approximate object matching. The RAGs to compare are first transformed into DAGs by orienting edges. Then, we compute some combinatoric metrics on nodes in order to classify them by similarity. At the end, a top-down process on DAGs aims to match similar patterns that exist between the two DAGs. The results are compared with those of a method based on relaxation matching.