Skip to main content
SpringerLink
Log in

Large-scale high-dimensional indexing by sparse hashing with l 0 approximation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this paper we propose a large-scale high-dimensional indexing algorithm based on sparse approximation and inverted indexing. Our goal was to devise a method that smoothly scales to handle databases with over 100 million descriptors on a single machine. To meet this goal, we implemented an inverted indexed based on a sparsifying dictionary with l 0 regression to assign documents to buckets. The sparsifying dictionary is optimized to reduce the data dimensionality, by concentrating the energy of the original vector on a few coefficients of a higher dimensional representation. These descriptors are added to an inverted index explores the locality of the coefficients of sparse representations to enable efficient pruned search. Evaluation on four large-scale datasets with multiple types of features showed that our method compares favorably to state-of-the-art techniques. On a 100 million dataset of SIFT descriptors, our method achieved 47.6 % precision at 50, by inspecting only 1 % of the full dataset, and by using only 1/20 of the time of a linear search.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. http://corpus-texmex.irisa.fr/

  2. http://www.cs.ubc.ca/research/flann/

  3. http://sglab.kaist.ac.kr/Spherical_Hashing/

  4. http://vcl.iti.gr/msidx/

References

  1. Aharon M, Elad M, Bruckstein A (2006) K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54 (11):4311–4322

    Article  MATH  Google Scholar 

  2. Andoni A, Indyk P (2008) Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun ACM 51(1):117–122

    Article  Google Scholar 

  3. Babenko A, Lempitsky V (2015) The inverted multi-index. IEEE Trans Pattern Anal Mach Intell 37(6):1247–1260

    Article  Google Scholar 

  4. Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Img Sci 2(1):183–202

    Article  MATH  MathSciNet  Google Scholar 

  5. Borges P, Mourão A, Magalhães J (2015) High-dimensional indexing by sparse approximation. In: Proceedings of the ACM ICMR. ACM, pp 163–170

  6. Candes E J, Tao T (2005) Decoding by linear programming. IEEE Trans Inf Theory 51(12):4203–4215

    Article  MATH  MathSciNet  Google Scholar 

  7. Chum O, Philbin J, Zisserman A (2008) Near duplicate image detection: min-hash and tf-idf weighting. In: British machine vision conference

  8. Ciaccia P, Patella M, Zezula P (1997) M-tree: an efficient access method for similarity search in metric spaces. In: Proceedings of the 23rd international conference on very large data bases, VLDB ’97. Morgan Kaufmann Publishers Inc, San Francisco, pp 426–435

  9. Datar M, Immorlica N, Indyk P, Mirrokni V S (2004) Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the twentieth annual symposium on computational geometry, SCG ’04. ACM, New York, pp 253–262

  10. Donoho D L, Elad M (2002) Optimally sparse representation in general (non-orthogonal) dictionaries via l1 minimization. In: Proc. Natl Acad. Sci.

  11. Gionis A, Indyk P, Motwani R (1999) Similarity search in high dimensions via hashing. In: Proceedings of the 25th international conference on very large data bases, VLDB ’99. Morgan Kaufmann Publishers Inc, San Francisco, pp 518–529

  12. Heo J-P, Lee Y, He J, Chang S-F, Yoon S-E (2012) Spherical hashing. In: 2012 IEEE Conference on computer vision and pattern recognition (CVPR), pp 2957–2964

  13. Hinton G E, Salakhutdinov R R (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

    Article  MATH  MathSciNet  Google Scholar 

  14. Jégou H, Douze M, Schmid C (2008) Hamming embedding and weak geometric consistency for large scale image search. In: Proceedings of the 10th European conference on computer vision: Part I, ECCV ’08. Berlin, Heidelberg, pp 304–317

    Google Scholar 

  15. Jégou H, Douze M, Schmid C (2011) Product quantization for nearest neighbor search. IEEE Trans Pattern Anal Mach Intell 33(1):117–128

    Article  Google Scholar 

  16. Jégou H, Furon T, Fuchs J-J (2012) Anti-sparse coding for approximate nearest neighbor search. In: ICASSP, pp 2029–2032

  17. Jégou H, Tavenard R, Douze M, Amsaleg L (2011) Searching in one billion vectors: re-rank with source coding. ArXiv e-prints

  18. Li Z, Ning H, Cao L, Zhan T, Gong Y, Huang T S (2011) Learning to search efficiently in high dimensions. In: Neural information processing systems

  19. Mourão A, Magalhães J (2015) Scalable multimodal search with distributed indexing by sparse hashing. In: Proceedings of ACM ICMR. ACM, pp 283–290

  20. Muja M, Lowe D G (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

  21. Navarro G (2002) Searching in metric spaces by spatial approximation. VLDB J 11:28–46

    Article  Google Scholar 

  22. Nocedal J, Wright S J (2000) Numerical optimization. Springer

  23. Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42:145–175

    Article  MATH  Google Scholar 

  24. Pati Y, Rezaiifar R, Krishnaprasad P (1993) Orthogonal matching pursuit: recursive function approximation with application to wavelet decomposition. In: Asilomar conf. on signals, systems and computer

  25. Raginsky M, Lazebnik S (2009) Locality-sensitive binary codes from shift-invariant kernels. In: NIPS, pp 1509–1517

  26. Schapire R E (1990) The strength of weak learnability. Mach Learn 5:197–227

    Google Scholar 

  27. Tavenard R, Jégou H, Amsaleg L (2011) Balancing clusters to reduce response time variability in large scale image search. In: International workshop on content-based multimedia indexing (CBMI 2011). QUAERO, Madrid

  28. Tiakas E, Rafailidis D, Dimou A, Daras P (2013) Msidx: multi-sort indexing for efficient content-based image search and retrieval. IEEE Trans Multimed 15(6):1415–1430

    Article  Google Scholar 

  29. Torralba A, Fergus R, Freeman W (2008) 80 million tiny images: a large data set for nonparametric object and scene recognition. IEEE Trans Pattern Anal Mach Intell 30(11):1958–1970

    Article  Google Scholar 

  30. Torralba A, Fergus R, Weiss Y (2008) Small codes and large image databases for recognition. In: IEEE Conference on computer vision and pattern recognition, 2008. CVPR 2008, pp 1–8

  31. Wang J, Kumar S, Chang S F (2012) Semi-supervised hashing for large-scale search. IEEE Trans Pattern Anal Mach Intell 34(12):2393–2406

    Article  Google Scholar 

  32. 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 24th international conference on very large data bases, VLDB ’98. Morgan Kaufmann Publishers Inc, San Francisco , pp 194–205

    Google Scholar 

  33. Weiss Y, Torralba A, Fergus R (2008) Spectral hashing. NIPS 9(1):6

    Google Scholar 

  34. Xia Y, He K, Wen F, Sun J (2013) Joint inverted indexing. In: IEEE International conference on computer vision, pp 3416–3423

  35. Yianilos P N (1993) Data structures and algorithms for nearest neighbor search in general metric spaces. In: Proceedings of the fourth annual ACM-SIAM symposium on discrete algorithms, SODA ’93, Philadelphia, pp 311–321

  36. Zhang J, Peng Y, Zhang J (2016) SSDH: semi-supervised deep hashing for large scale image retrieval. ArXiv e-prints

  37. Zhang L, Zhang Q, Zhang L, Tao D, Huang X, Du B (2015) Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding. Pattern Recogn 48(10):3102–3112

    Article  Google Scholar 

  38. Zheng L, Wang S, Liu Z, Tian Q (2014) Packing and padding: coupled multi-index for accurate image retrieval. In: IEEE Conference on computer vision and pattern recognition, pp 1947–1954

Download references

Acknowledgments

We would like to thank Microsoft Research for providing us with a Microsoft Azure Research Award sponsorship, which enabled us to do larger scale indexing experiments. This work has been partially funded by the projects PTDC/EIA-EIA/111518/2009, UTA-Est/MAI/0010/2009 and NOVA LINCS UID/CEC/04516/2013, funded by the Portuguese National Foundation for Science and Technology (FCT).

Author information

Authors and Affiliations

  1. NOVA LINCS, Department of Computer Science, Faculty of Science and Technology, Universidade Nova de Lisboa, Lisboa, Portugal

    Pedro Borges, André Mourão & João Magalhães

Authors
  1. Pedro Borges
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. André Mourão
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. João Magalhães
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to André Mourão.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Borges, P., Mourão, A. & Magalhães, J. Large-scale high-dimensional indexing by sparse hashing with l 0 approximation. Multimed Tools Appl 76, 24389–24412 (2017). https://doi.org/10.1007/s11042-016-4152-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-4152-1

Keywords

Search

Navigation