Skip to main content
SpringerLink
Log in

IHIRD: A Data Set for Indian Heritage Image Retrieval

  • Chapter
  • First Online:
Digital Techniques for Heritage Presentation and Preservation

Abstract

Computational approaches are extensively applied to preserve heritage artifacts. Archival of heritage assets is one of its primary focuses, accounting for the ease of accessibility of digital information. To develop an efficient and reliable archival-and-retrieval system of heritage images, it is necessary to have an extensive data set of heritage artifacts, which consists of various kinds of monuments in their digital representation. In this chapter, we develop an image data set, specifically on Indian heritage monuments, called Indian Heritage Image Retrieval Data set (IHIRD), and test it on several retrieval methods. Images of various heritage monuments, like sculptures and paintings, are the elements of this data set. We experimentally evaluate various content-based image retrieval (CBIR) and semantically driven CBIR schemes using this data set and report their performances.

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 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover 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

Notes

  1. 1.

    http://www.ignca.nic.in/dlrich.html.

  2. 2.

    http://www.asikolkata.in/bankura.aspx.

  3. 3.

    As a typical case study.

References

  1. Arandjelovic, R., Zisserman, A.: All about VLAD. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, CVPR ’13, pp. 1578–1585. IEEE Computer Society, Washington (2013)

    Google Scholar 

  2. Arandjelovic, R., Gronat, P., Torii, A., Pajdla, T., Sivic, J.: NetVLAD: CNN architecture for weakly supervised place recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5297–5307 (2016)

    Google Scholar 

  3. Babenko, A., Lempitsky, V.: Aggregating deep convolutional features for image retrieval (2015). Preprint, arXiv:1510.07493

    Google Scholar 

  4. Babenko, A., Slesarev, A., Chigorin, A., Lempitsky, V.: Neural codes for image retrieval. In: European Conference on Computer Vision, pp. 584–599. Springer, Berlin (2014)

    Google Scholar 

  5. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  6. Collins, E., Süsstrunk, S.: Deep feature factorization for content-based image retrieval and localization. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 874–878. IEEE, Piscataway (2019)

    Google Scholar 

  7. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893, June 2005

    Google Scholar 

  8. Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry, pp. 253–262. ACM, New York (2004)

    Google Scholar 

  9. ElAlami, M.: A new matching strategy for content based image retrieval system. Appl. Soft Comput. 14, 407–418 (2014)

    Article  Google Scholar 

  10. Ghorai, M., Purkait, P., Santra, S., Samanta, S., Chanda, B.: Bishnupur heritage image dataset (BHID): a resource for various computer vision applications. In: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing, pp. 80. ACM, New York (2016)

    Google Scholar 

  11. Gong, Y., Wang, L., Guo, R., Lazebnik, S.: Multi-scale orderless pooling of deep convolutional activation features. In: European Conference on Computer Vision, pp. 392–407. Springer, Berlin (2014)

    Google Scholar 

  12. Gupta, U., Chaudhury, S.: Deep transfer learning with ontology for image classification. In: 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp. 1–4 (2015)

    Google Scholar 

  13. Hosking, J.R., Wallis, J.R., Wood, E.F.: Estimation of the generalized extreme-value distribution by the method of probability-weighted moments. Technometrics 27(3), 251–261 (1985)

    Article  MathSciNet  Google Scholar 

  14. Jegou, H., Douze, M., Schmid, C.: 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, pp. 304–317. Springer, Berlin (2008)

    Google Scholar 

  15. Jégou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3304–3311, June 2010

    Google Scholar 

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

    Article  Google Scholar 

  17. Jégou, H., Perronnin, F., Douze, M., Sánchez, J., Pérez, P., Schmid, C.: Aggregating local image descriptors into compact codes. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1704–1716 (2012)

    Article  Google Scholar 

  18. Jiang, K., Que, Q., Kulis, B.: Revisiting kernelized locality-sensitive hashing for improved large-scale image retrieval. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4933–4941, June 2015

    Google Scholar 

  19. Kang, S., Song, J.: Parameter and quantile estimation for the generalized Pareto distribution in peaks over threshold framework. J. Korean Stat. Soc. 46(4), 487–501 (2017)

    Article  MathSciNet  Google Scholar 

  20. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  21. Mallik, A., Chaudhury, S., Ghosh, H.: Nrityakosha: preserving the intangible heritage of Indian classical dance. J. Comput. Cult. Herit. 4(3), 11 (2011)

    Article  Google Scholar 

  22. Mallik, A., Chaudhury, S., Madan, S., Dinesh, T., Chandru, U.V.: Archiving mural paintings using an ontology based approach. In: Asian Conference on Computer Vision, pp. 37–48 (2012)

    Google Scholar 

  23. Mangaonkar, P.: Temples of Bengal: material style and technological evolution. In: Chitrolekha International Magazine on Art and Design, pp. 3–17 (2012)

    Google Scholar 

  24. Michell, G.: Architecture and Art of Southern India: Vijayanagara and the Successor States 1350-1750, vol. 6. Cambridge University Press, Cambridge (1995)

    Book  Google Scholar 

  25. Morel, J.-M., Yu, G.: Asift: a new framework for fully affine invariant image comparison. SIAM J. Imaging Sci. 2(2), 438–469 (2009)

    Article  MathSciNet  Google Scholar 

  26. Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 2161–2168 (2006)

    Google Scholar 

  27. Panda, J., Sharma, S., Jawahar, C.V.: Heritage app: annotating images on mobile phones. In: Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP ’12, pp. 3:1–3:8. ACM, New York (2012)

    Google Scholar 

  28. Perronnin, F., Liu, Y., Sánchez, J., Poirier, H.: Large-scale image retrieval with compressed fisher vectors. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3384–3391, June 2010

    Google Scholar 

  29. Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) Computer Vision – ECCV 2010, pp. 143–156. Springer, Berlin (2010)

    Chapter  Google Scholar 

  30. Podder, D., Mukherjee, J., Aswatha, S.M., Mukherjee, J., Sural, S.: Ontology-driven content-based retrieval of heritage images. In: Heritage Preservation, pp. 143–160. Springer, Berlin (2018)

    Google Scholar 

  31. Sivic, J., Zisserman, A.: Video Google: efficient visual search of videos. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds.) Toward Category-Level Object Recognition. Lecture Notes in Computer Science, vol. 4170, pp. 127–144. Springer, Berlin (2006)

    Chapter  Google Scholar 

  32. Tang, J., Li, Z., Wang, M., Zhao, R.: Neighborhood discriminant hashing for large-scale image retrieval. IEEE Trans. Image Process. 24(9), 2827–2840 (2015)

    Article  MathSciNet  Google Scholar 

  33. Tolias, G., Sicre, R., Jégou, H.: Particular object retrieval with integral max-pooling of CNN activations (2015). Preprint, arXiv:1511.05879

    Google Scholar 

  34. Zheng, L., Yang, Y., Tian, Q.: Sift meets CNN: a decade survey of instance retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 40(5), 1224–1244 (2017)

    Article  Google Scholar 

  35. Zuva, K., Zuva, T.: Evaluation of image retrieval systems. Int. J. Comput. Sci. Inf. Technol. 4(3), 35–43 (2012)

    Google Scholar 

Download references

Acknowledgements

This work was carried out under the sponsorship of the Department of Science and Technology, Govt. of India, through sanction number NRDMS/11/1586/2009.

Author information

Authors and Affiliations

  1. Advanced Technology Development Center, Indian Institute of Technology, Kharagpur, Kharagpur, India

    Dipannita Podder & M. A. Shashaank

  2. Computer Science and Engineering, Indian Institute of Technology, Kharagpur, Kharagpur, India

    Jayanta Mukherjee & Shamik Sural

Authors
  1. Dipannita Podder
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. A. Shashaank
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jayanta Mukherjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shamik Sural
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Kharagpur, Kharagpur, India

    Jayanta Mukhopadhyay

  2. Delhi Technological University, Delhi, India

    Indu Sreedevi

  3. Indian Statistical Institute Kolkata, Kolkata, West Bengal, India

    Bhabatosh Chanda

  4. Indian Institute of Technology Jodhpur, Jodhpur, India

    Santanu Chaudhury

  5. Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    Vinay P. Namboodiri

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Podder, D., Shashaank, M.A., Mukherjee, J., Sural, S. (2021). IHIRD: A Data Set for Indian Heritage Image Retrieval. In: Mukhopadhyay, J., Sreedevi, I., Chanda, B., Chaudhury, S., Namboodiri, V.P. (eds) Digital Techniques for Heritage Presentation and Preservation. Springer, Cham. https://doi.org/10.1007/978-3-030-57907-4_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-57907-4_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57906-7

  • Online ISBN: 978-3-030-57907-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation