Skip to main content
Springer Nature Link
Log in

An efficient image retrieval method using adaptive weights

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

In existing image retrieval systems using a multipoint query, the neighborhood of the various optimal query points is determined in the same way. The different nature of the various optimal query points was not taken into consideration. This approach confines the performance of the system. To overcome this confinement, in this paper, we propose an image retrieval method through adaptive weights (Aweight) which is possible to compute the various optimal query points, optimal weights and improved distance functions to improve accuracy. In addition, our method constructs clusters without re-clustering the whole feedback image set. The experiments were performed on a set of 10,800 images and the results demonstrate that the proposed method improves performance of system in terms of accuracy.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Zhou XS, Huang T (2000) CBIR: from low-level features to high-level semantics. In: Proceedings of the SPIE, image and video communication and processing, San Jose, vol 3974, pp 426– 431

  2. Smeulders A, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans PAMI 22(12):1349–1380

    Article  Google Scholar 

  3. Xiao Z, Qi X (2014) Complementary relevance feedback-based content-based image retrieval. Multimed Tools Appl 73(2):2157–2177

    Article  Google Scholar 

  4. Rui Y, Huang T, Ortega M, Mehrotra S (1998) Relevance feedback: a power tool in interactive content-based image retrieval. IEEE Trans Circ Syst Video Technol 8(5):644–655

    Article  Google Scholar 

  5. Andre B, Vercauteren T, Buchner AM, Wallace MB, Ayache N (2012) Learning semantic and visual similarity for endomicroscopy video retrieval. IEEE Trans Med Imaging 31(6):1276–88

    Article  Google Scholar 

  6. Liu D, Hua KA, Vu K, Yu N (2009) Fast query point movement techniques for large CBIR systems. IEEE Trans Knowl Data Eng 21(5):729–743

    Article  Google Scholar 

  7. Vu HV, Quynh NH (2014) An image retrieval method using homogeneous region and relevance feedback. In: International conference on communication and signal processing, pp 114–118

  8. Ishikawa Y, Subramanya R, Faloutsos C (1998) Mind reader: querying databases through multiple examples. In: Proceedings of the 24th VLDB conference, New York, pp 218–227

  9. Norton D, Heath D, Ventura D (2016) Annotating images with emotional adjectives using features that summarize local interest points. IEEE Trans Affect Comput, Under Review

  10. Rui Y, Huang T, Mehrotra S (1997) Content-based image retrieval with relevance feedback in MARS. In: Proceedings of IEEE international conference on image processing, Santa Barbara

  11. Gevers T, Smeulders A (2004) Content-based image retrieval: an overview. In: Medioni G, Kang S B (eds) Emerging topics in computer vision. Prentice Hall, Englewood Cliffs

  12. Zhang L, Shum HPH, Shao L (2016) Discriminative semantic subspace analysis for relevance feedback. IEEE Trans Image Process 25(3):1275–1287

    Article  MathSciNet  Google Scholar 

  13. Chum O, Philbin J, Sivic J, Isard M, Zisserman A (2007) Total recall: automatic query expansion with a generative feature model for object retrieval. In: Proceedings of the ICCV

  14. Porkaew K, Chakrabarti K (1999) Query refinement for multimedia similarity retrieval in MARS. In: Proceedings of the 7th ACM multimedia conference, Orlando, pp 235–238

  15. Arandjelovíc R, Zisserman A (2012). In: Proceedings of the CVPR, Three things everyone should know to improve object retrieval

  16. Charikar M, Chekuri C, Feder T, Motwani R (1997) Incremental clustering and dynamic information retrieval. In: Proceedings of the ACM STOC conference, pp 626–635

  17. Rocchio JJ (1971) Relevance feedback in information retrieval. In: Salton G (ed) The SMART retrieval system—experiments in automatic document processing. Prentice Hall, Englewood Cliffs, pp 313–323

  18. Chakrabarti K, Michael O-B, Mehrotra S, Porkaew K (2004) Evaluating refined queries in top-k retrieval systems. IEEE Trans Knowl Data Eng 16(1):256–270

    Article  Google Scholar 

  19. Hua KA, Yu N, Liu D (2006) Query decomposition: a multiple neighborhood approach to relevance feedback processing in content-based image retrieval. In: Proceedings of the IEEE ICDE conference

  20. Jin X, French JC (2005) Improving image retrieval effectiveness via multiple queries. Multimed Tools Appl 26:221–245

    Article  Google Scholar 

  21. Thuy QDT, Huu QN, Van CP, Quoc TN (2017) An efficient semantic—related image retrieval method. Exp Syst Appl 72:30–41

  22. French JC, Martin WN, Watson JVS, Jin X (2003) Using multiple image representations to improve the quality of content-based image retrieval. Dept. of Computer Science, University of Virgina Technical Report CS-2003-10

  23. Jacobs DW, Weinshall D, Gdalyahu Y (2000) Classification with nonmetric distances: image retrieval and class representation. IEEE Trans Pattern Anal Mach Intell 22(6):583–600

    Article  Google Scholar 

  24. Chen Y, Wang JZ, Krovetz R (2003) An unsupervised learning app- roach to content-based image retrieval. In: Seventh international symposium on signal processing and its applications ISSPA, Paris

  25. Santos E Jr, Santos EE, Nguyen H, Pan L, Korah J (2011) A large-scale distributed framework for information retrieval in large dynamic search spaces. Appl Intell 35(3):375–398

    Article  Google Scholar 

  26. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22 (8):888–905

    Article  Google Scholar 

  27. Weiss Y (1999) Segmentation using eigenvectors: a unifying view. In: Proceedings of the international conference computer vision, pp 975–982

  28. Gdalyahu Y, Weinshall D, Warman M (2001) Self-organization in vision: stochastic clustering for image segmentation, perceptual grouping, and image database organization. IEEE Trans Pattern Anal Mach Intell 23 (10):1053–1074

    Article  Google Scholar 

  29. Ng AY, Jordan MI, Weiss Y (2001) On spectral clustering: analysis and algorithm. In: Proceedings of neural information processing systems (NIPS)

  30. Costeira J, Kanade T (1995) A multibody factorization method for motion analysis, pp 1071–1076

  31. Sarkar S, Soundararajan P (2000) Supervised learning of large percep-tual organization: graph spectral partitioning and learning automata. IEEE Trans Pattern Anal Mach Intell 22(5):504–525

    Article  Google Scholar 

  32. Duda RO, Hart PE (1972) Pattern classification and scene analysis. Wiley, New York

    MATH  Google Scholar 

  33. Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New York

    Book  MATH  Google Scholar 

  34. Rencher AC (2002) Methods of multivariate analysis. Wiley, New York

    Book  MATH  Google Scholar 

  35. Golub GH, Van Loan CF (1996) Matrix computations, 3rd edn. The Johns Hopkins University Press, Baltimore and London

    Google Scholar 

  36. Mehmood Z, Mahmood T, Javid MA (2017) Content-based image retrieval and semantic automatic image annotation based on the weighted average of triangular histograms using support vector machine. Appl Intell 48:166–181

  37. Castellano G, Fanelli AM, Sforza G, Torsello MA (2004) Content-based image retrieval: an overview. In: Medioni G, Kang S B (eds) Emerging topics in computer vision. Prentice Hall, Englewood Cliffs

  38. Lang B, Wu B, Liu Y, Liu X, Zhang B (2017) Fast graph similarity search via hashing and its application on image retrieval. Multimed Tools Appl. https://doi.org/10.1007/s11042-017-5194-8

  39. Chen Y, Wang JZ, Krovetz R (2005) CLUE: cluster-based retrieval of images by unsupervised learning. IEEE Trans Image Process 14(8):1187–1201

    Article  Google Scholar 

  40. Liu X, Huang L, Deng C, Lu J, Lang B (2015) Multi-view complementary hash tables for nearest neighbor search. In: IEEE International conference on computer vision, pp 1107–1115

Download references

Acknowledgments

The author gratefully acknowledges the many helpful suggestions of the anonymous reviewers during the preparation of the paper. This research was supported by the “Research on image retrieval based on multi queries” under grant no.PTNTŁD .17.04, and by the key laboratory Network Technology and Multimedia, Institute of Information Technology, Vietnam Academy of Science and Technology. The authors express their gratitude to Dr. Can Nguyen Van for supporting the financial part of this article.

Author information

Authors and Affiliations

  1. Information Technology Faculty, Electric Power University, Hanoi, Viet Nam

    Quynh Nguyen Huu & Canh Phuong Van

  2. Thai Nguyen University of Science, Quyet Thang, Thai Nguyen, Viet Nam

    Quynh Dao Thi Thuy

  3. The People’s Police University of Technology and Logistic, Thuan Thanh, Viet Nam

    Can Nguyen Van

  4. Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi, Viet Nam

    Tao Ngo Quoc

Authors
  1. Quynh Nguyen Huu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Quynh Dao Thi Thuy
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Canh Phuong Van
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Can Nguyen Van
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Tao Ngo Quoc
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Quynh Dao Thi Thuy.

Appendices

Appendix A

The average accuracy of the AWEIGHT method in each of the 80 categories

Table 2 The average accuracy of the AWEIGHT method in each of the 80 categories with three iterations
Full size table

Appendix B

The retrieval interface with the initial query image that has ID 84025 of the category pl_flower in the Corel Photo Gallery

Fig. 14
figure 14

The resulting interface implements the initial query. The result set contains 40 retrieved relevant images over 100 retrieved images

Full size image
Fig. 15
figure 15

The top results after one feedback iteration for the initial query. The result set contains 77 retrieved relevant images over 100 retrieved images (Images with red borders in Figs. 14, 15, 16 and 17 imply the same category art_dino with the query image)

Full size image
Fig. 16
figure 16

The top results after two feedback iterations for the initial query. The result set contains 86 retrieved relevant images over 100 retrieved images

Full size image
Fig. 17
figure 17

The top results after three feedback iterations for the initial query. The result set contains 100 retrieved relevant images over 100 retrieved images

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huu, Q.N., Thuy, Q.D.T., Phuong Van, C. et al. An efficient image retrieval method using adaptive weights. Appl Intell 48, 3807–3826 (2018). https://doi.org/10.1007/s10489-018-1174-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-018-1174-6

Keywords