Content Based Video Retrieval by Using Distributed Real-Time System Based on Storm

Abstract

Time processing is a challenging issue for content-based video retrieval systems, especially when the process of indexing, classifying and retrieving desired and relevant videos is from a huge database. A CBVR system called bounded coordinate of motion histogram (BCMH) has been implemented as a case study. The BCMH offline step requires a long time to complete the learning phase, and the online step falls short in addressing the real-time video processing. To overcome these drawbacks, this article presents a batch-oriented computing based on Apache Hadoop to improve the time processing for the offline step, and a real-time oriented computing based on Apache Storm topologies to achieve a real-time response for the online step. The proposed approach is tested on the HOLLYWOOD2 dataset and the obtained results demonstrate reliability and efficiency of the proposed method.

A distributed Content-Based Video Retrieval system for large datasets

“…In [26], we developed a video retrieval system based on motion histogram and residual data of the video content using batch processing based on Apache Hadoop. In addition, in [27], a distributed real-time processing has been proposed. However, these two works used the entire video to create and compare the video signatures, which reduced significantly the precision and the processing time.…”

“…To evaluate the performance of the proposed approach, we compared our results with those of three other distributed processing approaches including Udding et al [81], Xu et al [82], Wang et al [83], and Saoudi et al [27]. These approaches were performed on a distributed architecture using clusters of 4, 3, 9, and 10 machines, respectively.…”