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Improving retrieval accuracy of Hierarchical Cellular Trees for generic metric spaces

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Abstract

Metric Access Methods (MAMs) are indexing techniques which allow working in generic metric spaces. Therefore, MAMs are specially useful for Content-Based Image Retrieval systems based on features which use non L p norms as similarity measures. MAMs naturally allow the design of image browsers due to their inherent hierarchical structure. The Hierarchical Cellular Tree (HCT), a MAM-based indexing technique, provides the starting point of our work. In this paper, we describe some limitations detected in the original formulation of the HCT and propose some modifications to both the index building and the search algorithm. First, the covering radius, which is defined as the distance from the representative to the furthest element in a node, may not cover all the elements belonging to the node’s subtree. Therefore, we propose to redefine the covering radius as the distance from the representative to the furthest element in the node’s subtree. This new definition is essential to guarantee a correct construction of the HCT. Second, the proposed Progressive Query retrieval scheme can be redesigned to perform the nearest neighbor operation in a more efficient way. We propose a new retrieval scheme which takes advantage of the benefits of the search algorithm used in the index building. Furthermore, while the evaluation of the HCT in the original work was only subjective, we propose an objective evaluation based on two aspects which are crucial in any approximate search algorithm: the retrieval time and the retrieval accuracy. Finally, we illustrate the usefulness of the proposal by presenting some actual applications.

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References

  1. Ahmad I, Gabbouj M (2011) A generic content-based image retrieval framework for mobile devices. Multimed Tools Appl 55:423–442

    Article  Google Scholar 

  2. Andoni A, Indyk P (2008) Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun ACM 51:117

    Article  Google Scholar 

  3. Bayer R, McCreight EM (1972) Organization and maintenance of large ordered indexes. Acta Inform 1(3):173–189

    Article  Google Scholar 

  4. Bentley JL (1975) Multidimensional binary search trees used for associative searching. Commun ACM 18:509–517

    Article  MATH  Google Scholar 

  5. CENIT Buscamedia Project (2012) www.cenitbuscamedia.es

  6. Chávez E, Navarro G, Baeza-Yates R, Marroquín JL (2001) Searching in metric spaces. ACM Comput Surv (CSUR) 33:273–321

    Article  Google Scholar 

  7. Chierichetti F, Panconesi A, Raghavan P, Sozio M, Tiberi A, Upfal E (2007) Finding near neighbors through cluster pruning. In: Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems, PODS ’07. ACM, New York, USA, pp 103–112

    Chapter  Google Scholar 

  8. Ciaccia P, Patella M, Rabitti F, Zezula P (1997) Indexing metric spaces with mtree. In: Proc. Quinto convegno Nazionale SEBD, pp 67–86

  9. Fagin R, Kumar R, Sivakumar D (2003) Comparing top k lists. In: Proceedings of the fourteenth annual ACM-SIAM symposium on discrete algorithms, SODA ’03. Society for Industrial and Applied Mathematics. Philadelphia, USA, pp 28–36

  10. Geraci F (2007) Fast clustering for web information retrieval. Ph.D. thesis, Universita degli Studio di Siena, Facoltá di Ingegnieria, Dipartaminto di Ingegnieria dell’Informazione

  11. Giró X, Ventura C, Pont-Tuset J, Cortés S, Marqués F (2010) System architecture of a web service for content-based image retrieval. In: ACM international conference on image and video retrieval 2010, pp 358–365

  12. Indyk P, Motwani R (1998) Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the thirtieth annual ACM symposium on theory of computing, STOC ’98. ACM, New York, USA, pp 604–613

    Chapter  Google Scholar 

  13. Kendall MG (1970) Rank correlation methods, 4th edn. In: Kendall K (ed). Griffin, London (English)

  14. Kiranyaz S, Gabbouj M (2005) Novel multimedia retrieval technique: progressive query (why wait?) IEEE Proc Vision Image Signal Process 152:356–366

    Article  Google Scholar 

  15. Kiranyaz S, Gabbouj M (2007) Hierarchical cellular tree: an efficient indexing scheme for content-based retrieval on multimedia databases. IEEE Trans Multimedia 9:102–119

    Article  Google Scholar 

  16. Kiranyaz S, Ince T, Pulkkinen J, Gabbouj M, Ärje J, Kärkkäinen S, Tirronen V, Juhola M, Turpeinen T, Meissner K (2011) Classification and retrieval on macroinvertebrate image databases. Comput Biol Med 41:463–472

    Article  Google Scholar 

  17. Ling H, Okada K (2007) An efficient earth mover’s distance algorithm for robust histogram comparison. IEEE Trans Pattern Anal Mach Intell 29:840–853

    Article  Google Scholar 

  18. Lowe DG (1999) Object recognition from local scale-invariant features. In: Proceedings of the seventh IEEE international conference on computer vision, vol 2, pp 1150–1157

  19. Muja M, Lowe DG (2009) Fast approximate nearest neighbors with automatic algorithm configuration. In: International conference on computer vision theory and application VISSAPP’09. INSTICC Press, pp 331–340

  20. Niblack W, Barber R, Equitz W, Flickner M, Glasman EH, Petkovic D, Yanker P, Faloutsos C, Taubin G (1993) The qbic project: Querying images by content, using color, texture, and shape. In: Niblack W (ed) Proceedings SPIE storage and retrieval for image and video databases, vol. 1908, pp 173–187

  21. Novak D, Kyselak M, Zezula P (2010) On locality-sensitive indexing in generic metric spaces. In: Proceedings of the third international conference on similarity search and applications, SISAP ’10. ACM, New York, USA, pp 59–66

    Chapter  Google Scholar 

  22. Patella M, Ciaccia P (2009) Approximate similarity search: a multi-faceted problem. J Discrete Algorithms 7:36–48

    Article  MATH  MathSciNet  Google Scholar 

  23. Pele O, Werman M (2008) A linear time histogram metric for improved sift matching. In: Proceedings of the 10th European conference on computer vision: part III, ECCV ’08. Springer, Heidelberg, pp 495–508

    Google Scholar 

  24. Rubner Y, Tomasi C, Guibas LJ (2000) The earth mover’s distance as a metric for image retrieval. Int J Comput Vis 40:99–121

    Article  MATH  Google Scholar 

  25. Salembier P, Manjunath BS, Sikora T (2002) Introduction to mpeg-7, multimedia content description interface. Wiley, Ltd

  26. Singitham PKC, Mahabhashyam MS, Raghavan P (2004) Efficiency-quality tradeoffs for vector score aggregation. In: Proceedings of the thirtieth international conference on very large data bases, VLDB ’04, vol 40. VLDB Endowment, pp 624–635

  27. Ventura C, Martos M, Giró X, Vilaplana V, Marqués F (2012) Hierarchical navigation and visual search for video keyframe retrieval. In: Proceedings of the 18th international conference on advances in multimedia modeling, MMM’12. Springer, Heidelberg, pp 652–654

    Google Scholar 

  28. Weber R, Schek H-J, Blott S (1998) A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In: Proceedings of the 24rd international conference on very large data bases, VLDB ’98. Morgan Kaufmann Publishers Inc., San Francisco, USA, pp 194–205

    Google Scholar 

  29. Yang JC, Ding XR (2013) Movie audio retrieval based on HCT. Appl Mech Mater 321–324:1129–1132

    Google Scholar 

  30. Yang J-C, Li Y-X, Feng XH, He QH, He J (2011) Speaker retrieval based on minimum distance in HCT. In: IET international communication conference on wireless mobile and computing, (CCWMC 2011), pp 274–277

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Acknowledgements

This work was partially founded by the Catalan Broadcasting Corporation through the Spanish project CENIT-2009-1026 BuscaMedia, by TEC2010-18094 MuViPro project of the Spanish Government, and by FPU-2010 Research Fellowship Program of the Spanish Ministry of Education.

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  1. Technical University of Catalonia (UPC), Barcelona, Spain

    Carles Ventura, Verónica Vilaplana, Xavier Giró-i-Nieto & Ferran Marqués

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  1. Carles Ventura
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  2. Verónica Vilaplana
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  3. Xavier Giró-i-Nieto
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  4. Ferran Marqués
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Correspondence to Carles Ventura.

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Ventura, C., Vilaplana, V., Giró-i-Nieto, X. et al. Improving retrieval accuracy of Hierarchical Cellular Trees for generic metric spaces. Multimed Tools Appl 73, 1983–2008 (2014). https://doi.org/10.1007/s11042-013-1686-3

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