Indexing for Image Retrieval: A Machine Learning Based Approach

  • Santanu Chaudhury
  • Ehtesham Hassan
Part of the Communications in Computer and Information Science book series (CCIS, volume 276)


In this paper, we explore the use of machine learning for multimedia indexing and retrieval involving single/multiple features. Indexing of large image collection has been well researched problem. However, machine learning for combination of features in image indexing and retrieval framework is not explored. In this context, the paper presents novel formulation of multiple kernel learning in hashing for multimedia indexing. The framework learns combination of multiple features/ modalities for defining composite document indices in genetic algorithm based framework. We have demonstrated the evaluation of framework on dataset of handwritten digit images. Subsequently, the utility of the framework is explored for development for multi-modal retrieval of document images.


Image Retrieval Hash Table Document Image Mean Average Precision Kernel Space 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Rakotomamonjy, A., Bach, F., Canu, S., Grandvalet, Y.: More efficiency in multiple kernel learning. In: Proceedings of the ICML, vol. 772, pp. 775–782 (2007)Google Scholar
  2. 2.
    Sonneburg, S., Ratsch, G., Schafer, C., Scholkopf, B.: Large scale multiple kernel learning. Journal of Machine Learning Research 7, 1531–1565 (2006)Google Scholar
  3. 3.
    Lanckriet, G.R., Cristianini, N., Bartlett, P., Ghaoui, L.E., Jordan, M.I.: Learning the kernel matrix with semi-definite programming. Journal of Machine Learning Research 4, 27–72 (2004)Google Scholar
  4. 4.
    Bhm, C., Berchtold, S., Keim, D.A.: Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases. ACM Computing Surveys 33(3) (2001)Google Scholar
  5. 5.
    Indyk, P., Motwani, R.: Approximate nearest neighbor - towards removing the curse of dimensionality. In: Proceedings of the 30th ACM Symposium on Theory of Computing, pp. 604–613 (1998)Google Scholar
  6. 6.
    Mehmod, T.S.: Indexing of handwritten document images. In: Proceedings of the 1997 Workshop on Document Image Analysis, pp. 66–73 (1997)Google Scholar
  7. 7.
    Andoni, A., Indyk, P.: Near optimal hashing algorithms for approximate nearest neighbor in high dimensions. Communications of the ACM 51(1), 117–122 (2008)CrossRefGoogle Scholar
  8. 8.
    Haghani, P., Michel, S., Aberer, K.: Distributed similarity search in high dimensions using locality sensitive hashing. In: Proceedings of the 12th International Conference on Extending Database Technology, pp. 744–755 (2009)Google Scholar
  9. 9.
    Shen, H., Li, T., Schweiger, T.: An efficient similarity searching scheme in massive databases. In: Proceedings of the 3rd International Conference on Digital Telecommunications, pp. 47–52 (2008)Google Scholar
  10. 10.
    Weihong, W., Song, W.: A scalable content-based image retrieval scheme using locality-sensitive hashing. In: Proceedings of the International Conference on Computational Intelligence and Natural Computing, vol. 1, pp. 151–154 (2009)Google Scholar
  11. 11.
    Matei, B., Shan, Y., Sawhney, H.S., Tan, Y., Kumar, R., Huber, D., Hebert, M.: Rapid object indexing using locality sensitive hashing and joint 3d-signature space estimation. IEEE Transactions on PAMI 28(7), 1111–1126 (2006)CrossRefGoogle Scholar
  12. 12.
    Lv, Q., Josephson, W., Wang, Z., Charikar, M., Li, K.: Multi-probe lsh: Efficient indexing for high-dimensional similarity search. In: Proceedings of the 33rd International Conference on Very Large Data Bases, pp. 950–961 (2007)Google Scholar
  13. 13.
    Vassilis, A., Michalis, P., Panagiotis, P., George, K.: Nearest neighbor retrieval using distance based hashing. In: Proceedings of the 24th International Conference on Data Engineering, pp. 327–336 (April 2008)Google Scholar
  14. 14.
    Chun, Y.D., Seo, S.Y., Kim, N.C.: Image retrieval using bdip and bvlc moments. IEEE Transactions on Circuits and Systems for Video Technology 13(9), 951–957 (2003)CrossRefGoogle Scholar
  15. 15.
    Chun, Y.D., Kim, N.C., Jang, I.H.: Content-based image retrieval using multi-resolution colour and texture features. IEEE Transactions on Multimedia 10(6), 1073–1084 (2008)CrossRefGoogle Scholar
  16. 16.
    Gosselin, P.H., Cord, M., Philipp-Foliguet, S.: Combining visual dictionary, kernel-based similarity and learning strategy for image category retrieval. Computer Vision and Image Understanding 110, 403–417 (2008)CrossRefGoogle Scholar
  17. 17.
    Bai, S., Li, L., Tan, C.L.: Keyword spotting in document images through word shape coding. In: Proceedings of the 10th ICDAR, pp. 331–335 (2009)Google Scholar
  18. 18.
    Dorairaj, R., Namuduri, K.R.: Compact combination of mpeg-7 color and texture descriptors for image retrieval. In: Proceedings of the 38th Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 387–391 (2004)Google Scholar
  19. 19.
    Gagaudakis, G., Rosin, P.L.: Incorporating shape into histograms for cbir. Pattern Recognition 35, 81–91 (2002)MATHCrossRefGoogle Scholar
  20. 20.
    Qi, X., Han, Y.: A novel fusion approach to content-based image retrieval. Pattern Recognition (38), 2449–2465 (2005)Google Scholar
  21. 21.
    Rath, T.M., Manmatha, R.: Features for word spotting in historical manuscripts. In: Proceedings of the 7th ICDAR, vol. 1, pp. 218–222 (2003)Google Scholar
  22. 22.
    Dasigi, V., Mann, R.C., Protopopescu, V.A.: Information fusion for text classification - an experimental comparison. Pattern Recognition 34, 2413–2425 (2001)MATHCrossRefGoogle Scholar
  23. 23.
    Lin, Y., Bhanu, B.: Evolutionary feature synthesis for object recognition. IEEE Transactions on SMC-Part C: Applications and Reviews 35(2), 156–171 (2005)Google Scholar
  24. 24.
    Lin, Y., Bhanu, B.: Object detection via feature synthesis using mdl-based genetic programming. IEEE Transactions on SMC-Part B: Cybernetics 35(3), 538–547 (2005)CrossRefGoogle Scholar
  25. 25.
    Shen, J., Shepherd, J., Ngu, A.H.H.: Towards effective content-based music retrieval with multiple acoustic feature combination. IEEE Transactions on Multimedia 8(6), 1179–1189 (2006)CrossRefGoogle Scholar
  26. 26.
    Basak, J., Bhattacharya, K., Chaudhury, S.: Multiple exemplar-based facial image retrieval using independent component analysis. IEEE Transactions on Image Processing 15(12), 3773–3783 (2006)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. IEEE Transactions on PAMI 27(8), 1265–1278 (2005)CrossRefGoogle Scholar
  28. 28.
    Gehler, P., Nowozin, S.: On feature combination for multiclass object classification. In: Proceedings of the IEEE ICCV, pp. 1–8 (2009)Google Scholar
  29. 29.
    de Campos, T.E., Babu, B.R., Varma, M.: Character recognition in natural images. In: Proceedings of International Conference on Computer Vision Theory and Applications (VISAPP) (February 2009)Google Scholar
  30. 30.
    Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. The MIT Press (2006)Google Scholar
  31. 31.
    Wang, J., Kumar, S., Chang, S.F.: Sequential projection learning for hashing with compact codes. In: Proceedings of ICML, pp. 1127–1134 (2010)Google Scholar
  32. 32.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Longman (Singapore) Private Limited (2000)Google Scholar
  33. 33.
    Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient based learning applied to document recognition. The Proceedings of IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  34. 34.
    Deb, K., Agrawal, S.: Understanding interactions among genetic algorithm parameters. In: Foundations of Genetic Algorithms 5, pp. 265–286. Morgan Kaufmann (1998)Google Scholar
  35. 35.
    Czarn, A., Macnish, C., Vijayan, K., Turlach, B.: Statistical exploratory analysis of genetic algorithms. IEEE Transactions on Evolutionary Computation 8 (2004)CrossRefGoogle Scholar
  36. 36.
    Manning, C.D., Raghavan, P., Schtze, H.: An Introduction to Information Retrieval. Cambridge University Press, Cambridge (2009)Google Scholar
  37. 37.
    Charikar, M.S.: Similarity estimation techniques from rounding algorithms. In: Proceedings of the 34th ACM STOC, pp. 380–388 (2002)Google Scholar
  38. 38.
    Garg, R., Hassan, E., Chaudhury, S., Gopal, M.: A crf based scheme for overlapping multi-colored text graphics separation. In: ICDAR, pp. 1215–1219 (2011)Google Scholar
  39. 39.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the IEEE ICCV, vol. 2, pp. 1150–1157 (1999)Google Scholar
  40. 40.
    Hassan, E., Chaudhury, S., Gopal, M.: Word shape descriptor-based document image indexing: a new dbh-based approach. IJDAR, 1–20 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Santanu Chaudhury
    • 1
  • Ehtesham Hassan
    • 1
  1. 1.Department of Electrical EngineeringIIT DelhiIndia

Personalised recommendations