Abstract
The paper studies the influence on the similarity by extracting and using m from n frames on videos, the purpose is to evaluate the amount of the proportion similarity between them, and propose a new Content-Based Video Retrieval (CBVR) system. The proposed system uses a Bounded Coordinate of Motion Histogram (BCMH) [1] to characterize videos which are represented by spatio-temporal features (eg. motion vectors) and the Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD). However, a global representation of a video is compared pairwise with all those of the videos in the Hollywood2 dataset using the k-nearest neighbors (KNN). Moreover, this approach is adaptive: a training procedure is presented, and an accuracy of 58.1% is accomplished in comparison with the state-of-the-art approaches on the dataset of 1707 movie clips.
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El Ouadrhiri, A.A., Andaloussi, S.J., Saoudi, E.M., Ouchetto, O., Sekkaki, A. (2018). Similarity Performance of Keyframes Extraction on Bounded Content of Motion Histogram. In: Tabii, Y., Lazaar, M., Al Achhab, M., Enneya, N. (eds) Big Data, Cloud and Applications. BDCA 2018. Communications in Computer and Information Science, vol 872. Springer, Cham. https://doi.org/10.1007/978-3-319-96292-4_37
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