Abstract
In the advent of video data explosion, to understand the concept of the video, knowledge of representative data selection and summarization has become essential. In this regard, application of video key frame detection is becoming increasingly critical. Key frame selection of videos is the process of selecting one or more informative frames that depict the essence of the video. In state of the art, researchers have experimented with shot importance measure [1], epitome-based methods [2], and sparse coding techniques [3] to find informative frames of video. We propose block sparse coding formulation, which exploits the temporal correlation of video frames within the sparse coding framework for key frames selection. We solved the block sparse coding formulation using the Alternating Direction Method of Multipliers (ADMM) optimization. We show the comparison of results obtained with the proposed method, state-of-the-art algorithm [3] and ground truth on TRECVID 2002 [4] dataset. Comparison results show 8x run time and 6% F-score improvement compared to state of the art.
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Reddy GogiReddy, H.S.S., Sinha, N. (2020). Video Key Frame Detection Using Block Sparse Coding. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1022. Springer, Singapore. https://doi.org/10.1007/978-981-32-9088-4_8
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DOI: https://doi.org/10.1007/978-981-32-9088-4_8
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