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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.

Near-duplicate video content has taken the large storage space in the age of big data. Without respecting the copyright ethic, social media users mirror, resize, and/or hide certain online video content and re-upload it as new data. This research aims to avoid the complex and high-dimensional matching and present an efficient approach for detecting near-duplicate videos, this detection is based on feature extraction using visual, motion, and high-level features. Fast and adaptive bidimensional empirical mode decomposition is used to preserve the relevant data to the furthest extent possible during the low/high-frequency transition and vice-versa. In addition, for a generic model, the invariant moments are added to the aforementioned features in order to reinforce them against different video transformations such as rotating and scaling. Furthermore, the video frames are divided into blocks with a fixed number of features, this set of features is represented by a signature, where its mean and standard deviation represents a single video map allowing easy similarity computation. The F1-score and accuracy are used to evaluate the results of this study; the relevant results are ranked by Top$$_{1}$$ 1 for the best result, and the five top-ranked results are presented by Top$$_{5}$$ 5 . Further, our result of Top$$_{1}$$ 1 reached over 80% on F1-score, with a difference of ±4% from the Top$$_{5}$$ 5 results, and it is over 90% on Accuracy using different datasets, such as UCF11, UCF50, and HDMB51.

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