Towards the Parallelization of Shot Detection - a Typical Video Mining Application Study

As digital video data becomes more pervasive, mining information from multimedia data becomes increasingly important, e.g., extraction of goal events in soccer game automatically from the video content. Though all of these advances in multimedia mining have shown great potential in daily life, the huge computational requirement prohibits its wide use in practice. As computer architecture evolves from uniprocessor to the era of multi-core processors, accelerating the multimedia application by exploiting thread level parallelism would be more promising to boost performance and provide more functionality. This paper presents three different parallel approaches, i.e., task level, data slicing and hybrid parallel scheme, to parallelize shot detection, a widely used application in the video mining system. The hybrid scheme, with the exploration of data level and task level parallelism, delivers much better performance than the other two schemes. Besides, we also employ several software optimization techniques, e.g. data blocking and thread affinity, to improve the performance by more than 50%. Experimental results indicate that there are no obvious parallel limiting factors in the hybrid parallel scheme. It scales well the increasing number of processors, and exhibits 13.6x speedup on 16-way processor system