Similar-Video Retrieval via Learned Exemplars and Time-Warped Alignment
New learning algorithms and systems for retrieving similar videos are presented. Each query is a video itself. For each video, a set of exemplars is machine-learned by new algorithms. Two methods were tried. The first and main one is the time-bound affinity propagation. The second is the harmonic competition which approximates the first. In the similar-video retrieval, the number of exemplar frames is variable according to the length and contents of videos. Therefore, each exemplar possesses responsible frames. By considering this property, we give a novel similarity measure which contains the Levenshtein distance (L-distance) as its special case. This new measure, the M-distance, is applicable to both of global and local alignments for exemplars. Experimental results in view of precision-recall curves show creditable scores in the region of interest.
KeywordsSimilar-video retrieval exemplar time-bound affinity propagation M-distance numerical label
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- 2.Matsuyama Laboratory: Waseda Image Searchable Viewer (2006), http://www.wiz.cs.waseda.ac.jp/rim/wisvi-e.html
- 3.Hu, W., Xie, N., Li, L., Zeng, X., Maybank, S.: A Survey on Visual Content-Based Video Indexing and Retrieval. IEEE Trans. SMC 41, 797–819 (2011)Google Scholar
- 9.NHK creative library, http://www1.nhk.or.jp/creative/
- 11.Cheng, L., Hou, Z.-G., Tan, M.: Relaxation Labeling Using an Improved Hopfield Neural Network. Lecture Notes in Control and Information Sciences (345), 430–439 (2006)Google Scholar