Abstract
Learning algorithms that harmonize standardized video similarity tools and an integrated system are presented. The learning algorithms extract exemplars reflecting time courses of video frames. There were five types of such clustering methods. Among them, this paper chooses a method called time-partition pairwise nearest-neighbor because of its reduced complexity. On the similarity comparison among videos whose lengths vary, the M-distance that can absorb the difference of the exemplar cardinalities is utilized both for global and local matching. Given the order-aware clustering and the M-distance comparison, system designers can build a basic similar-video retrieval system. This paper promotes further enhancement on the exemplar similarity that matches the video signature tools for the multimedia content description interface by ISO/IEC. This development showed the ability of the similarity ranking together with the detection of plagiarism of video scenes. Precision-recall curves showed a high performance in this experiment.
Y. Matsuyama—This work was supported by the Grant-in-Aid for Scientific Research 26330286, and Waseda University Special Research Projects 2015K-161.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Information technology – Multimedia content description interface, International Standard of ISO/IEC 15938-3, Amendment 4 (2010)
Matsuyama, Y., Shikano, A., Iwase, H., Horie, T.: Order-aware exemplars for structuring video sets: clustering, aligned matching and retrieval by similarity. In: Proceedings of IJCNN, TBA (2015)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)
Matsuyama, Y.: Harmonic competition: a self-organizing multiple criteria optimization. IEEE Trans. Neural Netw. 7, 652–668 (1996)
Equitz, W.: A new vector quantization algorithm. IEEE Trans. ASSP 37, 1568–1575 (1989)
Yahoo! Webscope dataset YFCC-100M. http://labs.yahoo.com/Academic_relations
Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Bio. 48, 443–453 (1970)
Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147, 195–197 (1981)
Pachalakis, S., Iwamoto, K., et al.: The MPEG-7 video signature tools for content identification. IEEE Trans. Circ. Syst. Video Tech. 22, 1050–1063 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Horie, T., Shikano, A., Iwase, H., Matsuyama, Y. (2015). Learning Algorithms and Frame Signatures for Video Similarity Ranking. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-26532-2_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26531-5
Online ISBN: 978-3-319-26532-2
eBook Packages: Computer ScienceComputer Science (R0)