Abstract
It is now accepted that the most effective video shot retrieval is based on indexing and retrieving clips using multiple, parallel modalities such as text-matching, image-matching and feature matching and then combining or fusing these parallel retrieval streams in some way. In this paper we investigate a range of fusion methods for combining based on multiple visual features (colour, edge and texture), for combining based on multiple visual examples in the query and for combining multiple modalities (text and visual). Using three TRECVid collections and the TRECVid search task, we specifically compare fusion methods based on normalised score and rank that use either the average, weighted average or maximum of retrieval results from a discrete Jelinek-Mercer smoothed language model. We also compare these results with a simple probability-based combination of the language model results that assumes all features and visual examples are fully independent.
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References
de Vries, A.P., Westerveld, T.: A comparison of continuous vs. discrete image models for probabilistic image and video retrieval. In: Proceedings of IEEE International Conference on Image Processing, (ICIP 2004) (2004)
Fox, E., Shaw, J.: Combination of multiple searches. In: Proceedings of the 2nd Text REtrieval Conference TREC-2, pp. 243–252 (1994); NIST Special Publications 500-215
Lee, J.H.: Analyses of multiple evidence combination. In: Proc. of the 20th Intl. Conf. on Research and Development in Information Retrieval (SIGIR 1997), pp. 267–276 (1997)
Manmatha, R., Feng, F., Rath, T.: Using models of score distributions in information retrieval. In: Proceedings of the LM Workshop 2001, pp. 91–96 (2001)
Savoy, J., Le Calve, A., Vrajitoru, D.: Report on the TREC-5 experiment: Data fusion and collection fusion. In: Proceedings of TREC-5, pp. 489–502 (1997)
Smeaton, A.F.: Independence of contributing retrieval strategies in data fusion for effective information retrieval. In: Proceedings of the 20th BCS-IRSG Colloquium, Grenoble, France. Springer, Heidelberg (1998); Workshops in Computing
Smith, J., Jaimes, A., Lin, C.-Y., Naphade, M., Natsev, A., Tseng, B.: Interactive search fusion methods for video database retrieval. In: IEEE International Conference on Image Processing (ICIP), pp. 741–744 (2003)
Westerveld, T., Ianeva, T., Boldareva, L., de Vries, A.P., Hiemstra, D.: Combining information sources for video retrieval: The Lowlands team at TRECVID 2003. In: Proceedings of TRECVid 2003 (2004)
Yan, R., Hauptmann, A.G.: Co-retrieval: A boosted reranking approach for video retrieval. In: Enser, P.G.B., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 60–69. Springer, Heidelberg (2004)
Yan, R., Yang, J., Hauptmann, A.G.: Learning query-class dependent weights in automatic video retrieval. In: Proceedings of ACM Multimedia 2004, New York, NY, pp. 548–555 (October 2004)
Yavlinsky, A., Pickering, M.J., Heesch, D., Rüger, S.: A comparative study of evidence combination strategies. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (2004)
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Donald, K.M., Smeaton, A.F. (2005). A Comparison of Score, Rank and Probability-Based Fusion Methods for Video Shot Retrieval. In: Leow, WK., Lew, M.S., Chua, TS., Ma, WY., Chaisorn, L., Bakker, E.M. (eds) Image and Video Retrieval. CIVR 2005. Lecture Notes in Computer Science, vol 3568. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11526346_10
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DOI: https://doi.org/10.1007/11526346_10
Publisher Name: Springer, Berlin, Heidelberg
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