Skip to main content

Query-by-Example Image Retrieval in Microsoft SQL Server

  • Conference paper
  • First Online:
Artificial Intelligence and Soft Computing (ICAISC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9693))

Included in the following conference series:

Abstract

In this paper we present a system intended for content-based image retrieval tightly integrated with a relational database management system. Users can send query images over the appropriate web service channel or construct database queries locally. The presented framework analyses the query image based on descriptors which are generated by the bag-of-features algorithm and local interest points. The system returns the sequence of similar images with a similarity level to the query image. The software was implemented in .NET technology and Microsoft SQL Server 2012. The modular construction allows to customize the system functionality to client needs but it is especially dedicated to business applications. Important advantage of the presented approach is the support by SOA (Service-Oriented Architecture), which allows to use the system in a remote way. It is possible to build software which uses functions of the presented system by communicating over the web service API with the WCF technology.

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

Access this chapter

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 EPUB and 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

References

  1. Akhtar, Z., Rattani, A., Foresti, G.L.: Temporal analysis of adaptive face recognition. J. Artif. Intell. Soft Comput. Res. 4(4), 243–255 (2014)

    Article  Google Scholar 

  2. 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 

  3. Chang, T., Kuo, C.C.: Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Process. 2(4), 429–441 (1993)

    Article  Google Scholar 

  4. Chaudhuri, S., Narasayya, V.R.: An efficient, cost-driven index selection tool for Microsoft SQL server. VLDB 97, 146–155 (1997)

    Google Scholar 

  5. Chu, J.L., Krzyzak, A.: The recognition of partially occluded objects with support vector machines and convolutional neural networks and deep belief networks. J. Artif. Intell. Soft Comput. Res. 4(1), 5–19 (2014)

    Article  Google Scholar 

  6. Drozda, P., Sopyła, K., Górecki, P.: Online crowdsource system supporting ground truth datasets creation. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS, vol. 7894, pp. 532–539. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  7. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)

    Article  Google Scholar 

  8. Francos, J., Meiri, A., Porat, B.: A unified texture model based on a 2-D Wold-like decomposition. IEEE Trans. Signal Process. 41(8), 2665–2678 (1993)

    Article  MATH  Google Scholar 

  9. Grauman, K., Darrell, T.: Efficient image matching with distributions of local invariant features. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 627–634, June 2005

    Google Scholar 

  10. Huang, J., Kumar, S., Mitra, M., Zhu, W.J., Zabih, R.: Image indexing using color correlograms. In: 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings, pp. 762–768, June 1997

    Google Scholar 

  11. Jagadish, H.V.: A retrieval technique for similar shapes. SIGMOD Rec. 20(2), 208–217 (1991)

    Article  Google Scholar 

  12. Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using gabor filters. Pattern Recogn. 24(12), 1167–1186 (1991)

    Article  Google Scholar 

  13. Kanimozhi, T., Latha, K.: An integrated approach to region based image retrieval using firefly algorithm and support vector machine. Neurocomputing 151, 1099–1111 (2015)

    Article  Google Scholar 

  14. Karakasis, E., Amanatiadis, A., Gasteratos, A., Chatzichristofis, S.: Image moment invariants as local features for content based image retrieval using the bag-of-visual-words model. Pattern Recogn. Lett. 55, 22–27 (2015)

    Article  Google Scholar 

  15. Kauppinen, H., Seppanen, T., Pietikainen, M.: An experimental comparison of autoregressive and Fourier-based descriptors in 2D shape classification. IEEE Trans. Pattern Anal. Mach. Intell. 17(2), 201–207 (1995)

    Article  Google Scholar 

  16. Kiranyaz, S., Birinci, M., Gabbouj, M.: Perceptual color descriptor based on spatial distribution: a top-down approach. Image Vis. Comput. 28(8), 1309–1326 (2010)

    Article  Google Scholar 

  17. Korytkowski, M., Scherer, R., Staszewski, P., Woldan, P.: Bag-of-features image indexing and classification in Microsoft SQL server relational database. In: 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), pp. 478–482 (2015)

    Google Scholar 

  18. Korytkowski, M., Rutkowski, L., Scherer, R.: Fast image classification by boosting fuzzy classifiers. Inf. Sci. 327, 175–182 (2016)

    Article  MathSciNet  Google Scholar 

  19. Larson, P., Clinciu, C., Hanson, E.N., Oks, A., Price, S.L., Rangarajan, S., Surna, A., Zhou, Q.: SQL server column store indexes. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, pp. 1177–1184. ACM (2011)

    Google Scholar 

  20. Lin, C.H., Chen, H.Y., Wu, Y.S.: Study of image retrieval and classification based on adaptive features using genetic algorithm feature selection. Expert Syst. Appl. 41(15), 6611–6621 (2014)

    Article  Google Scholar 

  21. Liu, G.H., Yang, J.Y.: Content-based image retrieval using color difference histogram. Pattern Recogn. 46(1), 188–198 (2013)

    Article  Google Scholar 

  22. Liu, J.: Image retrieval based on bag-of-words model (2013). arXiv preprint arXiv:1304.5168

  23. Liu, S., Bai, X.: Discriminative features for image classification and retrieval. Pattern Recogn. Lett. 33(6), 744–751 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10), 761–767 (2004). British Machine Vision Computing 2002

    Article  Google Scholar 

  26. Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vis. 60(1), 63–86 (2004)

    Article  Google Scholar 

  27. Murata, M., Ito, S., Tokuhisa, M., Ma, Q.: Order estimation of Japanese paragraphs by supervised machine learning and various textual features. J. Artif. Intell. Soft Comput. Res. 5(4), 247–255 (2015)

    Article  Google Scholar 

  28. Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006, vol. 2, pp. 2161–2168. IEEE, Computer Society, Washington, DC (2006)

    Google Scholar 

  29. O’Hara, S., Draper, B.A.: Introduction to the bag of features paradigm for image classification and retrieval (2011). arXiv preprint arXiv:1101.3354

  30. Pass, G., Zabih, R.: Histogram refinement for content-based image retrieval. In: Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision, WACV 1996, pp. 96–102, December 1996

    Google Scholar 

  31. Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8, June 2007

    Google Scholar 

  32. Rafiei, D., Mendelzon, A.O.: Efficient retrieval of similar shapes. VLDB J. 11(1), 17–27 (2002)

    Article  Google Scholar 

  33. Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: A simultaneous feature adaptation and feature selection method for content-based image retrieval systems. Knowl. Based Syst. 39, 85–94 (2013)

    Article  Google Scholar 

  34. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: An efficient alternative to sift or surf. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571, November 2011

    Google Scholar 

  35. Shrivastava, N., Tyagi, V.: Content based image retrieval based on relative locations of multiple regions of interest using selective regions matching. Inf. Sci. 259, 212–224 (2014)

    Article  Google Scholar 

  36. Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: Proceedings of the 2003 Ninth IEEE International Conference on Computer Vision, vol. 2, pp. 1470–1477, October 2003

    Google Scholar 

  37. Śmietański, J., Tadeusiewicz, R., Łuczyńska, E.: Texture analysis in perfusion images of prostate cancer–a case study. Int. J. Appl. Math. Comput. Sci. 20(1), 149–156 (2010)

    Google Scholar 

  38. Srinivasan, J., De Fazio, S., Nori, A., Das, S., Freiwald, C., Banerjee, J.: Index with entries that store the key of a row and all non-key values of the row. US Patent 6,128,610, 3 October 2000

    Google Scholar 

  39. Veltkamp, R.C., Hagedoorn, M.: State of the art in shape matching. In: Lew, M.S. (ed.) Principles of Visual Information Retrieval, pp. 87–119. Springer, London (2001)

    Chapter  Google Scholar 

  40. Voloshynovskiy, S., Diephuis, M., Kostadinov, D., Farhadzadeh, F., Holotyak, T.: On accuracy, robustness, and security of bag-of-word search systems. In: IS&T/SPIE Electronic Imaging, International Society for Optics and Photonics, p. 902807 (2014)

    Google Scholar 

  41. Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1794–1801, June 2009

    Google Scholar 

  42. Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. In: 2006 Conference on Computer Vision and Pattern Recognition Workshopp, CVPRW 2006, p. 13, June 2006

    Google Scholar 

  43. Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 391–405. Springer, Heidelberg (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rafał Scherer .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Staszewski, P., Woldan, P., Korytkowski, M., Scherer, R., Wang, L. (2016). Query-by-Example Image Retrieval in Microsoft SQL Server. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9693. Springer, Cham. https://doi.org/10.1007/978-3-319-39384-1_66

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-39384-1_66

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39383-4

  • Online ISBN: 978-3-319-39384-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics