Abstract
We introduce a method of searching the k nearest neighbours (k-NN) using PM-tree. The PM-tree is a metric access method for similarity search in large multimedia databases. As an extension of M-tree, the structure of PM-tree exploits local dynamic pivots (like M-tree does it) as well as global static pivots (used by LAESA-like methods). While in M-tree a metric region is represented by a hyper-sphere, in PM-tree the ”volume” of metric region is further reduced by a set of hyper-rings. As a consequence, the shape of PM-tree’s metric region bounds the indexed objects more tightly which, in turn, improves the overall search efficiency. Besides the description of PM-tree, we propose an optimal k-NN search algorithm. Finally, the efficiency of k-NN search is experimentally evaluated on large synthetic as well as real-world datasets.
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References
Böhm, C., Berchtold, S., Keim, D.: Searching in High-Dimensional Spaces – Index Structures for Improving the Performance of Multimedia Databases. ACM Computing Surveys 33(3), 322–373 (2001)
Bustos, B., Navarro, G., Chávez, E.: Pivot selection techniques for proximity searching in metric spaces. Pattern Recognition Letters 24(14), 2357–2366 (2003)
Chávez, E.: Optimal discretization for pivot based algorithms (Manuscript) (1999), ftp://garota.fismat.umich.mx/pub/users/elchavez/minimax.ps.gz
Chávez, E., Navarro, G., Baeza-Yates, R., Marroquín, J.: Searching in Metric Spaces. ACM Computing Surveys 33(3), 273–321 (2001)
Ciaccia, P., Patella, M., Zezula, P.: M-tree: An Efficient Access Method for Similarity Search in Metric Spaces. In: Proceedings of the 23rd Athens Intern. Conf. on VLDB, pp. 426–435. Morgan Kaufmann, San Francisco (1997)
Micó, M.L., Oncina, J., Vidal, E.: A new version of the nearest-neighbour approximating and eliminating search algorithm (aesa) with linear preprocessing time and memory requirements. Pattern Recognition Letters 15(1), 9–17 (1994)
Patella, M.: Similarity Search in Multimedia Databases. PhD thesis, University of Bologna (1999)
Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, San Jose, CA, pp. 71–79 (1995)
Shapiro, M.: The choice of reference points in best-match file searching. Commun. ACM 20(5), 339–343 (1977)
Skopal, T.: Metric Indexing in Information Retrieval. PhD thesis, Technical University of Ostrava (2004), http://urtax.ms.mff.cuni.cz/~skopal/phd/thesis.pdf
Skopal, T., Pokorný, J., Krátký, M., Snášel, V.: Revisiting M-tree Building Principles. In: Kalinichenko, L.A., Manthey, R., Thalheim, B., Wloka, U. (eds.) ADBIS 2003. LNCS, vol. 2798, pp. 148–162. Springer, Heidelberg (2003)
Skopal, T., Pokorný, J., Snášel, V.: PM-tree: Pivoting Metric Tree for Similarity Search in Multimedia Databases. In: Benczúr, A.A., Demetrovics, J., Gottlob, G. (eds.) ADBIS 2004. LNCS, vol. 3255, pp. 99–114. Springer, Heidelberg (2004)
WBIIS project: Wavelet-based Image Indexing and Searching, Stanford University, http://wang.ist.psu.edu/
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Skopal, T., Pokorný, J., Snášel, V. (2005). Nearest Neighbours Search Using the PM-Tree. In: Zhou, L., Ooi, B.C., Meng, X. (eds) Database Systems for Advanced Applications. DASFAA 2005. Lecture Notes in Computer Science, vol 3453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408079_73
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DOI: https://doi.org/10.1007/11408079_73
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