Abstract
Video summarization aims to manage video data by providing succinct representation of videos, however its evaluation is somewhat challenging. IMage Euclidean Distance (IMED) has been proposed for the measurement of the similarity of two images. Though it is effective and can tolerate the distortion and/or small movement of the objects, its computational complexity is high in the order of \(O(n^2)\). This paper proposes an efficient method for evaluating the video summaries. It retrieves a set of matched frames between automatic summary and the ground truth summary through two way search, in which the similarity between two frames are measured using the Efficient IMED (EIMED), which considers neighboring pixels, rather than all the pixels in the frames. Experimental results based on a publicly accessible dataset has shown that the proposed method is effective in finding precise matches and usually discards the false ones, leading to a more objective measurement of the performance for various techniques.
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Notes
- 1.
Open Video Project. http://www.open-video.org.
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Acknowledgements
The first author would like to thank for the award given by Aberystwyth University under the Departmental Overseas Scholarship (DOS) and partly funding by Object Matrix, Ltd on the project.
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Kannappan, S., Liu, Y., Tiddeman, B.P. (2016). Performance Evaluation of Video Summaries Using Efficient Image Euclidean Distance. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_4
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DOI: https://doi.org/10.1007/978-3-319-50832-0_4
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