Abstract
In this article, we present an approximate technique that allows accelerating similarity search in high dimensional vector spaces. The presented approach, called HiPeR, is based on a hierarchy of subspaces and indices: it performs nearest neighbors search across spaces of different dimensions, starting with the lowest dimensions up to the highest ones, aiming at minimizing the effects of the curse of dimensionality. In this work, HiPeR has been implemented on the classical index structure VA-File, providing VA-Hierarchies. The model of precision loss defined is probabilistic and non parametric and quality of answers can be selected by user at query time. HiPeR is evaluated for range queries on 3 real data-sets of image descriptors varying from 500,000 vectors to 4 millions. The experiments show that this approximate technique improves retrieval by saving I/O access significantly.
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References
Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In: FOCS, pp. 459–468 (2006)
Berchtold, S., Böhm, C., Kriegel, H.: The pyramid-technique: towards breaking the curse of dimensionality. In: SIGMOD, pp. 142–153 (1998)
Bouteldja, N., Gouet-Brunet, V., Scholl, M.: HiPeR: Hierarchical progressive exact retrieval in multi-dimensional spaces. In: SISAP, pp. 25–34 (2008)
Bouteldja, N., Gouet-Brunet, V., Scholl, M.: The many facets of progressive retrieval for CBIR. In: PCM (2008)
Bracewell, R.N.: The Fourier Transformation and its Applications, 2nd edn. McGraw-Hill, New York (1978)
Cha, G., Chung, C.: The GC-tree: a high-dimensional index structure for similarity search in image databases. IEEE Transactions on Multimedia, 235–247 (2002)
Ferhatosmanoglu, H., Tuncel, E., Agrawal, D., Abbadi, A.E.: Approximate nearest neighbor searching in multimedia databases. In: ICDE, USA, pp. 503–521 (2001)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. In: JMLS, pp. 1157–1182 (2003)
Lin, K., Jagadish, H.V., Faloutsos, C.: The tv-tree: An index structure for high-dimensional data. VLDB, 517–542 (1994)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV, 91–110 (2004)
Patella, M., Ciaccia, P.: The many facets of approximate similarity search. In: SISAP, pp. 10–21 (2008)
Samet, H.: Foundations of Multidimensional and Metric Data Structures. The Morgan Kaufmann Series in Computer Graphics (2006)
Swain, M.J., Ballard, D.H.: Color indexing. In: IJCV, pp. 11–32 (November 1991)
Urruty, T.: KpyrRec: a recursive multidimensional indexing structure. In: IJPEDS (2007)
Weber, R., Böhm, K.: Trading quality for time with nearest neighbor search. In: EDBT, pp. 21–35 (2000)
Weber, R., Schek, H., Blott, S.: A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In: VLDB, pp. 194–205 (1998)
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Bouteldja, N., Gouet-Brunet, V., Scholl, M. (2009). Approximate Retrieval with HiPeR: Application to VA-Hierarchies. In: Huet, B., Smeaton, A., Mayer-Patel, K., Avrithis, Y. (eds) Advances in Multimedia Modeling . MMM 2009. Lecture Notes in Computer Science, vol 5371. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92892-8_36
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DOI: https://doi.org/10.1007/978-3-540-92892-8_36
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