Abstract
TV commercial archives, once recorded the fashion and technology of our society, contain large amount of information deserved for deep analysis, for instance, discovery of hot products, exploration of the relationship between the air times and market sales of a product, analysis and prediction of the market trends, and so on. Levering a new text-to-features transformation and integrating many state-of-the-art video search techniques, we have built an interactive system on top of video retrieval in a large collection of three-year five-channel TV commercial videos. To the best of our knowledge, this is the largest commercial data set used for retrieval so far. To interact with the system, users can either use a keyboard to type keywords or use their mobile devices to snap a picture to describe their interested products, and the system will return relevant commercials in real time. Users are further able to browse videos and access their air patterns, such as air time and air frequency. This pattern usually reflects social behavior of viewers, i.e. which social groups (young or adult, male or female) are the targets of the product, when is the peak time for viewers to watch this commercial category according to the air pattern.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arandjelović, R., Zisserman, A.: Three things everyone should know to improve object retrieval. In: CVPR (2012)
Carbonell, J., Yang, Y., Frederking, R., Brown, R., Geng, Y., Lee, D.: Translingual information retrieval. In: IJCAI (1997)
Chum, O., Matas, J., Kittler, J.: Locally optimized RANSAC. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 236–243. Springer, Heidelberg (2003)
Colombo, C., Bimbo, A.D., Pala, P.: Retrieval of commercials by semantic content: The semiotic perspective. Multimedia Tools Appl. 13(1), 93–118 (2001)
Henze, N., Boll, S.: Snap and share your photobooks. In: ACM Multimedia (2008)
Lowe, D.: Distinctive image features from scale-invariant key points. IJCV 60, 91–110 (2004)
Paul, O., Awad, G., Michel, M., Fiscus, J., Kraaij, W., Smeaton, A.F., Quéenot, G.: Trecvid 2011 - an overview of the goals, tasks, data, evaluation mechanisms and metrics. In: TRECVID (2011)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: CVPR (2007)
Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: ICCV (2003)
Wu, X., Satoh, S.: Ultrahigh-speed tv commercial detection, extraction, and matching. IEEE Trans. Circuits Syst. Video Tech. 23(6), 1054–1069 (2013)
Yan, R., Hauptmann, A., Jin, R.: Multimedia search with pseudo-relevance feedback. In: Bakker, E.M., Lew, M., Huang, T.S., Sebe, N., Zhou, X.S. (eds.) CIVR 2003. LNCS, vol. 2728, Springer, Heidelberg (2003)
Zhu, C.-Z., Mei, T., Hua, X.-S.: Natural video browsing. In: ACM Multimedia (2005)
Zhu, C.-Z., Satoh, S.: Large vocabulary quantization for searching instances from videos. In: ICMR (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhu, CZ., Kasamwattanarote, S., Wu, X., Satoh, S. (2014). Tell Me about TV Commercials of This Product. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds) MultiMedia Modeling. MMM 2014. Lecture Notes in Computer Science, vol 8325. Springer, Cham. https://doi.org/10.1007/978-3-319-04114-8_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-04114-8_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04113-1
Online ISBN: 978-3-319-04114-8
eBook Packages: Computer ScienceComputer Science (R0)