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Partial Refinement for Similarity Search with Multiple Features

  • Marcel Zierenberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8821)

Abstract

Filter refinement is an efficient and flexible indexing approach to similarity search with multiple features. However, the conventional refinement phase has one major drawback: when an object is refined, the partial distances to the query object are computed for all features. This frequently leads to more distance computations being executed than necessary to exclude an object. To address this problem, we introduce partial refinement, a simple, yet efficient improvement of the filter refinement approach. It incrementally replaces partial distance bounds with exact partial distances and updates the aggregated bounds accordingly each time. This enables us to exclude many objects before all of their partial distances have been computed exactly. Our experimental evaluation illustrates that partial refinement significantly reduces the number of required distance computations and the overall search time in comparison to conventional refinement and other state-of-the-art techniques.

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References

  1. 1.
    Samet, H.: Foundations of Multidimensional and Metric Data Structures. The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling. Morgan Kaufmann Publishers Inc., San Francisco (2005)Google Scholar
  2. 2.
    Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. Advances in Database Systems, vol. 32, pp. 1–191. Springer-Verlag New York Inc., Secaucus (2006)Google Scholar
  3. 3.
    Böhm, K., Mlivoncic, M., Schek, H.-J., Weber, R.: Fast Evaluation Techniques for Complex Similarity Queries. In: Proc. of the 27th International Conference on Very Large Data Bases, VLDB 2001, pp. 211–220. Morgan Kaufmann Publishers Inc., San Francisco (2001)Google Scholar
  4. 4.
    Bustos, B., Keim, D., Schreck, T.: A Pivot-Based Index Structure for Combination of Feature Vectors. In: Proc. of the 2005 ACM Symposium on Applied Computing, SAC 2005, pp. 1180–1184. ACM, New York (2005)Google Scholar
  5. 5.
    Jagadish, H.V., Ooi, B.C., Shen, H.T., Tan, K.-L.: Toward Efficient Multifeature Query Processing. IEEE Trans. on Knowl. and Data Eng. 18, 350–362 (2006)CrossRefGoogle Scholar
  6. 6.
    Zierenberg, M., Bertram, M.: FlexiDex: Flexible Indexing for Similarity Search with Logic-Based Query Models. In: Catania, B., Guerrini, G., Pokorný, J. (eds.) ADBIS 2013. LNCS, vol. 8133, pp. 274–287. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Chávez, E., Navarro, G., Baeza-Yates, R., Marroquín, J.L.: Searching in Metric Spaces. ACM Comput. Surv. 33, 273–321 (2001)CrossRefGoogle Scholar
  8. 8.
    Carélo, C.C.M., Pola, I.R.V., Ciferri, R.R., Traina, A.J.M., Traina Jr., C., de Aguiar Ciferri, C.D.: Slicing the Metric Space to Provide Quick Indexing of Complex Data in the Main Memory. Inf. Syst. 36(1), 79–98 (2011)CrossRefGoogle Scholar
  9. 9.
    Fagin, R., Lotem, A., Naor, M.: Optimal Aggregation Algorithms for Middleware. In: Proc. of the 20th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2001, pp. 102–113. ACM, New York (2001)Google Scholar
  10. 10.
    Zellhöfer, D., Schmitt, I.: A Preference-Based Approach for Interactive Weight Learning: Learning Weights Within a Logic-Based Query Language. Distributed and Parallel Databases 27, 31–51 (2010)CrossRefGoogle Scholar
  11. 11.
    Bustos, B., Kreft, S., Skopal, T.: Adapting Metric Indexes for Searching in Multi-Metric Spaces. Multimedia Tools Appl. 58(3), 467–496 (2012)CrossRefGoogle Scholar
  12. 12.
    Ciaccia, P., Patella, M.: The M2-tree: Processing Complex Multi-Feature Queries with Just One Index. In: DELOS Workshop: Information Seeking, Searching and Querying in Digital Libraries (2000)Google Scholar
  13. 13.
    Hjaltason, G.R., Samet, H.: Ranking in Spatial Databases. In: Egenhofer, M., Herring, J.R. (eds.) SSD 1995. LNCS, vol. 951, pp. 83–95. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  14. 14.
    Griffinn, G., Holub, A., Perona, P.: Caltech-256 Object Category Dataset. Tech. rep. 7694. California Institute of Technology (2007)Google Scholar
  15. 15.
    Villegas, M., Paredes, R., Thomee, B.: Overview of the ImageCLEF 2013 Scalable Concept Image Annotation Subtask. In: CLEF 2013 Evaluation Labs and Workshop, Online Working Notes, Valencia, Spain (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Marcel Zierenberg
    • 1
  1. 1.Institute of Computer Science, Information and Media Technology, Chair of Database and Information SystemsBrandenburg University of Technology Cottbus – SenftenbergCottbusGermany

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