Abstract
We evaluate the application of feature-vector based image retrieval methods to the problem of video retrieval. A vast number of primitive features is calculated for each of the key frames generated by a segmentation process, and we examine the use of three methods for retrieving video segments using the features — a vector space model, a learning method using the AdaBoost algorithm, and a k-nearest neighbour approach.
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© 2002 Springer-Verlag Berlin Heidelberg
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Pickering, M.J., Rüger, S.M., Sinclair, D. (2002). Video Retrieval by Feature Learning in Key Frames. In: Lew, M.S., Sebe, N., Eakins, J.P. (eds) Image and Video Retrieval. CIVR 2002. Lecture Notes in Computer Science, vol 2383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45479-9_33
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DOI: https://doi.org/10.1007/3-540-45479-9_33
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