Multimedia Database Technologies for Sophisticated Analysis of Large-Scale Video Corpora

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The cost of building a huge multimedia archive has been significantly reduced. Especially, broadcast video archives are useful for multimedia research since they involve a wide variety of contents. It is naturally perceived that a huge amount of broadcast video would be a useful corpus for multimedia indexing and mining research. Text corpora are widely used for text processing research. They contribute to the advancement of text processing techniques. The same phenomenon may arise for multimedia information processing. However, in general, video analysis is exhaustively time consuming. It is quite common to take one hour for one PC to analyze one-hour video. This means that we need hundreds of PCs to process thousands hours of video in a day. In near future, it may be common to process thousands of videos with hundreds of CPU cores. Thus, HPC and DB Technologies would also be an important element for large-scale multimedia information processing. From this viewpoint, we conducted a preliminary study on multimedia database technologies for analyzing large-scale video corpora. We extracted about 550, 000 camera shots from our video archive which records a daily news program for seven years (2, 400 days/1, 200 hours). By choosing a representative frame from each camera shot, we obtained a collection containing wide variety of images. Then, we applied our indexing techniques, including SR-Tree (a multidimensional index structure) and DSNN (distinctiveness-sensitive nearest-neighbor search algorithm), to examine the effectiveness of database technologies. According to our performance experiments, we confirmed that database technologies are quite effective for reducing both computation and I/O costs of multimedia indexing.