Abstract
In this paper, we consider the problem of finding the k most similar objects given a query object, in large multimedia datasets. We focus on scenarios where the similarity measure itself is not fixed, but is continuously being refined with user feedback. Conventional database techniques for efficient similarity search are not effective in this environment as they take a specific similarity/distance measure as input and build index structures tuned for that measure. Our approach works effectively in this environment as validated by the experimental study where we evaluate it over a wide range of datasets. The experiments show it to be efficient and scalable. In fact, on all our datasets, the response times were within a few seconds, making our approach suitable for interactive applications.
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References
Huang, T., Zhou, X.: Image retrieval with relevance feedback: From heuristic weight adjustment to optimal learning methods. In: International Conference on Image Processing, pp. III:2–5 (2001)
Rui, Y., Thomas, S.H., Chang, S.F.: Image retrieval: Past, present, and future. In: International Symposium on Multimedia Information Processing (1997)
Yang, J., Li, Q., Zhuang, Y.: Towards data-adaptive and user-adaptive image retrieval by peer indexing. International Journal of Computer Vision 56, 47–63 (2004)
Bentley, J.L.: Multidimensional binary search trees used for associative searcing. Communications of the ACM 18, 509–517 (1975)
Guttman, A.: R-trees: A dynamic index structure for spatial searching. In: ACM SIGMOD International Conference on Management of Data, pp. 47–57 (1984)
White, D.A., Jain, R.: Similarity indexing with ss-tree. In: Proc. 12th International conference on Data Engineering, New Orleans, Louisiana (1996)
Faloutsos, C.: Searching multimedia databases by content. In: Advances in Database Systems. Kluwer Academic Publishers, Boston (1996)
Faloutsos, C., Equitz, W., Flickner, M., Niblack, W., Petkovic, D., Barber, R.: Efficient and effective querying in image content. J. of Intelligent Information Systems, 231–262 (1994a)
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. In: ACM SIGMOD International Conference on Management of Data, pp. 419–429 (1994b)
Kurniawati, R., Jin, J., Shepherd, J.A.: Efficient nearest-neighbor searches using weighted euclidean metrics. Technical report, Information Engineering Department, School of Computer science and Engineering, University of New South Wales (1998)
Rui, Y., Huang, T.S., Mehrotra, S.: Relevance feedback techniques in interactive content-based image retrieval. In: IS&T and SPIE Storage and Retrieval of Image and Video Databases VI, San Jose, CA, USA (January 1998)
Tian, Q., Hong, P., Huang, T.: Update relevant image weights for content-based image retrieval using support vector machines. In: IEEE Inter. Conf. on Multimedia & Expo, vol. 18, pp. 1199–1202 (2000)
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© 2006 Springer-Verlag Berlin Heidelberg
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Jammalamadaka, N., Pudi, V., Jawahar, C.V. (2006). Efficient Search with Changing Similarity Measures on Large Multimedia Datasets. In: Cham, TJ., Cai, J., Dorai, C., Rajan, D., Chua, TS., Chia, LT. (eds) Advances in Multimedia Modeling. MMM 2007. Lecture Notes in Computer Science, vol 4352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69429-8_21
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DOI: https://doi.org/10.1007/978-3-540-69429-8_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69428-1
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