Skip to main content

Learning Binary Hash Codes for Large-Scale Image Search

  • Chapter

Part of the Studies in Computational Intelligence book series (SCI,volume 411)

Abstract

Algorithms to rapidly search massive image or video collections are critical for many vision applications, including visual search, content-based retrieval, and non-parametric models for object recognition. Recent work shows that learned binary projections are a powerful way to index large collections according to their content. The basic idea is to formulate the projections so as to approximately preserve a given similarity function of interest. Having done so, one can then search the data efficiently using hash tables, or by exploring the Hamming ball volume around a novel query. Both enable sub-linear time retrieval with respect to the database size. Further, depending on the design of the projections, in some cases it is possible to bound the number of database examples that must be searched in order to achieve a given level of accuracy.

This chapter overviews data structures for fast search with binary codes, and then describes several supervised and unsupervised strategies for generating the codes. In particular, we review supervised methods that integrate metric learning, boosting, and neural networks into the hash key construction, and unsupervised methods based on spectral analysis or kernelized random projections that compute affinity-preserving binary codes.Whether learning from explicit semantic supervision or exploiting the structure among unlabeled data, these methods make scalable retrieval possible for a variety of robust visual similarity measures.We focus on defining the algorithms, and illustrate the main points with results using millions of images.

Keywords

  • Hash Function
  • Binary Code
  • Hash Table
  • Neural Information Processing System
  • Locality Sensitive Hashing

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-28661-2_3
  • Chapter length: 39 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-28661-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   139.99
Price excludes VAT (USA)
Hardcover Book
USD   169.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Andoni, A., Indyk, P.: Near-Optimal Hashing Algorithms for Near Neighbor Problem in High Dimensions. In: IEEE Symposium on Foundations of Computer Science, FOCS (2006)

    Google Scholar 

  2. Athitsos, V., Alon, J., Sclaroff, S., Kollios, G.: BoostMap: A Method for Efficient Approximate Similarity Rankings. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2004)

    Google Scholar 

  3. Athitsos, V., Alon, J., Sclaroff, S., Kollios, G.: BoostMap: An Embedding Method for Efficient Nearest Neighbor Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 30(1) (2008)

    Google Scholar 

  4. Babenko, B., Branson, S., Belongie, S.: Similarity Metrics for Categorization: from Monolithic to Category Specific. In: Proceedings of the IEEE International Conference on Computer Vision, ICCV (2009)

    Google Scholar 

  5. Bar-Hillel, A., Hertz, T., Shental, N., Weinshall, D.: Learning a Mahalanobis Metric from Equivalence Constraints. Journal of Machine Learning Research 6, 937–965 (2005)

    MathSciNet  MATH  Google Scholar 

  6. Belkin, M., Niyogi, P.: Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. In: Neural Information Processing Systems (NIPS), pp. 585–591 (2001)

    Google Scholar 

  7. Belkin, M., Niyogi, P.: Towards a theoretical foundation for laplacian based manifold methods. J. of Computer System Sciences (2007)

    Google Scholar 

  8. Bengio, Y., Paiement, J.-F., Vincent, P., Delalleau, O., Le Roux, N., Ouimet, M.: Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering. In: Neural Information Processing Systems, NIPS (2004)

    Google Scholar 

  9. Bentley, J.: Multidimensional Divide and Conquer. Communications of the ACM 23(4), 214–229 (1980)

    MathSciNet  MATH  CrossRef  Google Scholar 

  10. Broder, A.: On the Resemblance and Containment of Documents. In: Proceedings of the Compression and Complexity of Sequences (1997)

    Google Scholar 

  11. Bronstein, M., Bronstein, A., Michel, F., Paragios, N.: Data Fusion through Cross-modality Metric Learning using Similarity-Sensitive Hashing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2010)

    Google Scholar 

  12. Charikar, M.: Similarity Estimation Techniques from Rounding Algorithms. In: ACM Symp. on Theory of Computing (2002)

    Google Scholar 

  13. Chum, O., Perdoch, M., Matas, J.: Geometric min-Hashing: Finding a (Thick) Needle in a Haystack. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2009)

    Google Scholar 

  14. Chum, O., Philbin, J., Zisserman, A.: Near Duplicate Image Detection: min-Hash and tf-idf Weighting. In: British Machine Vision Conference (2008)

    Google Scholar 

  15. Coifman, R., Lafon, S., Lee, A.B., Maggioni, M., Nadler, B., Warner, F., Zucker, S.W.: Geometric Diffusions as a Tool for Harmonic Analysis and Struture Definition of Data: Diffiusion Maps. Proc. Natl. Academy of Sciences 102(21), 7426–7431 (2005)

    CrossRef  Google Scholar 

  16. Crammer, K., Keshet, J., Singer, Y.: Kernel Design Using Boosting. In: Neural Information Processing Systems, NIPS (2002)

    Google Scholar 

  17. Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.: Locality-Sensitive Hashing Scheme Based on p-Stable Distributions. In: Symposium on Computational Geometry, SOCG (2004)

    Google Scholar 

  18. Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image Retrieval: Ideas, Influences, and Trends of the New Age. ACM Computing Surveys (2008)

    Google Scholar 

  19. Davis, J., Kulis, B., Jain, P., Sra, S., Dhillon, I.: Information-Theoretic Metric Learning. In: Proceedings of International Conference on Machine Learning, ICML (2007)

    Google Scholar 

  20. Fergus, R., Bernal, H., Weiss, Y., Torralba, A.: Semantic Label Sharing for Learning with Many Categories. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 762–775. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  21. Fowlkes, C., Belongie, S., Chung, F., Malik, J.: Spectral Grouping Using the Nystrom Method. PAMI 26(2), 214–225 (2004)

    CrossRef  Google Scholar 

  22. Freidman, J., Bentley, J., Finkel, A.: An Algorithm for Finding Best Matches in Logarithmic Expected Time. ACM Transactions on Mathematical Software 3(3), 209–226 (1977)

    CrossRef  Google Scholar 

  23. Gionis, A., Indyk, P., Motwani, R.: Similarity Search in High Dimensions via Hashing. In: Proc. Intl Conf. on Very Large Data Bases (1999)

    Google Scholar 

  24. Globerson, A., Roweis, S.: Metric Learning by Collapsing Classes. In: Neural Information Processing Systems, NIPS (2005)

    Google Scholar 

  25. Goemans, M., Williamson, D.: Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming. JACM 42(6), 1115–1145 (1995)

    MathSciNet  MATH  CrossRef  Google Scholar 

  26. Goldberger, J., Roweis, S.T., Salakhutdinov, R.R., Hinton, G.E.: Neighborhood Components Analysis. In: Neural Information Processing Systems, NIPS (2004)

    Google Scholar 

  27. Grauman, K., Darrell, T.: The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features. In: Proceedings of the IEEE International Conference on Computer Vision, ICCV (2005)

    Google Scholar 

  28. Grauman, K., Darrell, T.: Pyramid Match Hashing: Sub-Linear Time Indexing Over Partial Correspondences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2007)

    Google Scholar 

  29. Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality Reduction by Learning an Invariant Mapping. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2006)

    Google Scholar 

  30. Hertz, T., Bar-Hillel, A., Weinshall, D.: Learning Distance Functions for Image Retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2004)

    Google Scholar 

  31. Hertz, T., Bar-Hillel, A., Weinshall, D.: Learning a Kernel Function for Classification with Small Training Samples. In: Proceedings of International Conference on Machine Learning, ICML (2006)

    Google Scholar 

  32. Hinton, G.E., Salakhutdinov, R.R.: Reducing the Dimensionality of Data with Neural Networks. Nature 313(5786), 504–507 (2006)

    MathSciNet  MATH  Google Scholar 

  33. Indyk, P., Motwani, R.: Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality. In: 30th Symposium on Theory of Computing (1998)

    Google Scholar 

  34. Indyk, P., Thaper, N.: Fast Image Retrieval via Embeddings. In: Intl. Workshop on Statistical and Computational Theories of Vision (2003)

    Google Scholar 

  35. Iqbal, Q., Aggarwal, J.K.: CIRES: A System for Content-Based Retrieval in Digital Image Libraries. In: International Conference on Control, Automation, Robotics and Vision (2002)

    Google Scholar 

  36. Jain, P., Kulis, B., Dhillon, I., Grauman, K.: Online Metric Learning and Fast Similarity Search. In: Neural Information Processing Systems, NIPS (2008)

    Google Scholar 

  37. Jain, P., Kulis, B., Grauman, K.: Fast Image Search for Learned Metrics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2008)

    Google Scholar 

  38. Kulis, B., Darrell, T.: Learning to Hash with Binary Reconstructive Embeddings. In: Neural Information Processing Systems, NIPS (2009)

    Google Scholar 

  39. Kulis, B., Grauman, K.: Kernelized Locality-Sensitive Hashing for Scalable Image Search. In: Proceedings of the IEEE International Conference on Computer Vision, ICCV (2009)

    Google Scholar 

  40. Kulis, B., Jain, P., Grauman, K.: Fast Similarity Search for Learned Metrics. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 31 (2009)

    Google Scholar 

  41. Lazebnik, S., Schmid, C., Ponce, J.: Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2006)

    Google Scholar 

  42. Lin, R.-S., Ross, D., Yagnik, J.: SPEC Hashing: Similarity Preserving Algorithm for Entropy-based Coding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2010)

    Google Scholar 

  43. Ling, H., Soatto, S.: Proximity Distribution Kernels for Geometric Context in Category Recognition. In: Proceedings of the IEEE International Conference on Computer Vision, ICCV (2007)

    Google Scholar 

  44. Liu, T., Moore, A., Gray, A., Yang, K.: An Investigation of Practical Approximate Nearest Neighbor Algorithms. In: Neural Information Processing Systems, NIPS (2005)

    Google Scholar 

  45. Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision (IJCV) 60(2) (2004)

    Google Scholar 

  46. Mu, Y., Shen, J., Yan, S.: Weakly-supervised hashing in kernel space. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2010)

    Google Scholar 

  47. Muja, M., Lowe, D.: Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration. In: International Conference on Computer Vision Theory and Application, VISSAPP (2009)

    Google Scholar 

  48. Nadler, B., Lafon, S., Coifman, R., Kevrekidis, I.: Diffusion maps, spectral clustering and reaction coordinates of dynamical systems (2008), http://arxiv.org

  49. Ng, A., Jordan, M.I., Weiss, Y.: On Spectral Clustering, Analysis and an Algorithm. In: Neural Information Processing Systems, NIPS (2001)

    Google Scholar 

  50. Oliva, A., Torralba, A.: Modeling the Shape of the Scene: a Holistic Representation of the Spatial Envelope. International Journal in Computer Vision 42, 145–175 (2001)

    MATH  CrossRef  Google Scholar 

  51. Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object Retrieval with Large Vocabularies and Fast Spatial Matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2007)

    Google Scholar 

  52. Raginsky, M., Lazebnik, S.: Locality-Sensitive Binary Codes from Shift-Invariant Kernels. In: Neural Information Processing Systems, NIPS (2009)

    Google Scholar 

  53. Rahimi, A., Recht, B.: Random Features for Large-Scale Kernel Machines. In: Neural Information Processing Systems, NIPS (2007)

    Google Scholar 

  54. Rice, J.: Mathematical Statistics and Data Aanalysis. Duxbury Press (2001)

    Google Scholar 

  55. Roweis, S., Saul, L.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290(5500), 2323–2326 (2000)

    CrossRef  Google Scholar 

  56. Salakhutdinov, R.R., Hinton, G.E.: Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure. In: AISTATS (2007)

    Google Scholar 

  57. Salakhutdinov, R.R., Hinton, G.E.: Semantic Hashing. In: SIGIR Workshop on Information Retrieval and Applications of Graphical Models (2007)

    Google Scholar 

  58. Schölkopf, B., Smola, A., Müller, K.-R.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 10, 1299–1319 (1998)

    CrossRef  Google Scholar 

  59. Schultz, M., Joachims, T.: Learning a Distance Metric from Relative Comparisons. In: Neural Information Processing Systems, NIPS (2003)

    Google Scholar 

  60. Shakhnarovich, G.: Learning Task-Specific Similarity. PhD thesis. MIT (2005)

    Google Scholar 

  61. Shakhnarovich, G., Viola, P., Darrell, T.: Fast pose estimation with parameter sensitive hashing. In: Proceedings of the IEEE International Conference on Computer Vision, ICCV (2003)

    Google Scholar 

  62. Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press (2004)

    Google Scholar 

  63. Sivic, J., Zisserman, A.: Video Google: A Text Retrieval Approach to Object Matching in Videos. In: Proceedings of the IEEE International Conference on Computer Vision, ICCV (2003)

    Google Scholar 

  64. Tenenbaum, J., de Silva, V., Langford, J.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290(5500), 2319–2323 (2000)

    CrossRef  Google Scholar 

  65. Torralba, A., Fergus, R., Weiss, Y.: Small Codes and Large Image Databases for Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2008)

    Google Scholar 

  66. Uhlmann, J.: Satisfying General Proximity/Similarity Queries with Metric Trees. Information Processing Letters 40, 175–179 (1991)

    MATH  CrossRef  Google Scholar 

  67. van der Maaten, L., Hinton, G.: Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9, 2579–2605 (2008)

    MATH  Google Scholar 

  68. Varma, M., Ray, D.: Learning the Discriminative Power-Invariance Trade-off. In: Proceedings of the IEEE International Conference on Computer Vision, ICCV (2007)

    Google Scholar 

  69. Wang, J., Kumar, S., Chang, S.-F.: Semi-Supervised Hashing for Scalable Image Retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2010)

    Google Scholar 

  70. Wang, J., Kumar, S., Chang, S.-F.: Sequential Projection Learning for Hashing with Compact Codes. In: Proceedings of International Conference on Machine Learning, ICML (2010)

    Google Scholar 

  71. Weinberger, K., Blitzer, J., Saul, L.: Distance Metric Learning for Large Margin Nearest Neighbor Classification. In: Neural Information Processing Systems, NIPS (2006)

    Google Scholar 

  72. Weiss, Y., Torralba, A., Fergus, R.: Spectral Hashing. In: Neural Information Processing Systems, NIPS (2008)

    Google Scholar 

  73. Xing, E., Ng, A., Jordan, M., Russell, S.: Distance Metric Learning, with Application to Clustering with Side-Information. In: Neural Information Processing Systems, NIPS (2002)

    Google Scholar 

  74. Xu, D., Cham, T.J., Yan, S., Chang, S.-F.: Near Duplicate Image Identification with Spatially Aligned Pyramid Matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2008)

    Google Scholar 

  75. Yeh, T., Grauman, K., Tollmar, K., Darrell, T.: A Picture is Worth a Thousand Keywords: Image-Based Object Search on a Mobile Platform. In: Proceedings of the ACM Conference on Human Factors in Computing Systems (2005)

    Google Scholar 

  76. Zhang, H., Berg, A., Maire, M., Malik, J.: SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2006)

    Google Scholar 

  77. Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study. International Journal of Computer Vision (IJCV) 73(2), 213–238 (2007)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kristen Grauman .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2013 Springer Berlin Heidelberg

About this chapter

Cite this chapter

Grauman, K., Fergus, R. (2013). Learning Binary Hash Codes for Large-Scale Image Search. In: Cipolla, R., Battiato, S., Farinella, G. (eds) Machine Learning for Computer Vision. Studies in Computational Intelligence, vol 411. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28661-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28661-2_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28660-5

  • Online ISBN: 978-3-642-28661-2

  • eBook Packages: EngineeringEngineering (R0)