Skip to main content
SpringerLink
Log in

Indexing for Image Retrieval: A Machine Learning Based Approach

  • Conference paper
Intelligent Interactive Technologies and Multimedia (IITM 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 276))

  • 952 Accesses

Abstract

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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

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

    Article  Google Scholar 

  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. 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. 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. 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)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. Gagaudakis, G., Rosin, P.L.: Incorporating shape into histograms for cbir. Pattern Recognition 35, 81–91 (2002)

    Article  MATH  Google Scholar 

  20. Qi, X., Han, Y.: A novel fusion approach to content-based image retrieval. Pattern Recognition (38), 2449–2465 (2005)

    Google Scholar 

  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. Dasigi, V., Mann, R.C., Protopopescu, V.A.: Information fusion for text classification - an experimental comparison. Pattern Recognition 34, 2413–2425 (2001)

    Article  MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  27. Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. IEEE Transactions on PAMI 27(8), 1265–1278 (2005)

    Article  Google Scholar 

  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. 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. Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. The MIT Press (2006)

    Google Scholar 

  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. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Longman (Singapore) Private Limited (2000)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. Czarn, A., Macnish, C., Vijayan, K., Turlach, B.: Statistical exploratory analysis of genetic algorithms. IEEE Transactions on Evolutionary Computation 8 (2004)

    Article  Google Scholar 

  36. Manning, C.D., Raghavan, P., Schtze, H.: An Introduction to Information Retrieval. Cambridge University Press, Cambridge (2009)

    Google Scholar 

  37. Charikar, M.S.: Similarity estimation techniques from rounding algorithms. In: Proceedings of the 34th ACM STOC, pp. 380–388 (2002)

    Google Scholar 

  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. 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. Hassan, E., Chaudhury, S., Gopal, M.: Word shape descriptor-based document image indexing: a new dbh-based approach. IJDAR, 1–20 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, IIT Delhi, India

    Santanu Chaudhury & Ehtesham Hassan

Authors
  1. Santanu Chaudhury
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ehtesham Hassan
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology, Allahabad (IIIT-A), Deoghat, Jhalwa, 211 012, Allahabad, India

    Anupam Agrawal

  2. Indian Institute of Information Technology, Allahabad (IIIT-A), Deoghat, Jhalwa, 211 012, Allahabad, India

    R. C. Tripathi  & M. D. Tiwari  & 

  3. ACME Lab, Design Computing and Human Centered Computing, School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, USA

    Ellen Yi-Luen Do

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chaudhury, S., Hassan, E. (2013). Indexing for Image Retrieval: A Machine Learning Based Approach. In: Agrawal, A., Tripathi, R.C., Do, E.YL., Tiwari, M.D. (eds) Intelligent Interactive Technologies and Multimedia. IITM 2013. Communications in Computer and Information Science, vol 276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37463-0_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37463-0_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37462-3

  • Online ISBN: 978-3-642-37463-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation