A new parallel video understanding and retrieval system

In this paper, a hybrid parallel computing framework is proposed for video understanding and retrieval. It is a unified computing architecture based on the Map-Reduce programming model, which supports multi-core and GPU architectures. A key task scheduler is designed for the parallelization of computation tasks. The SVM method is used to train models for video understanding purposes. To effectively shorten the training and processing time, the hybrid computing framework is used to train large scale SVM models. The TRECVID database is used as the basic experimental content for video understanding and retrieval. Experiments were conducted on two 8-core servers, each equipped with NVIDIA Quadro FX 4600 graphics card. Results proved that the proposed parallel computing framework works well for the video understanding and retrieval system by speeding up system development and providing better performances.