Skip to main content
SpringerLink
Log in

Image recommendation based on keyword relevance using absorbing Markov chain and image features

  • Regular Paper
  • Published:
International Journal of Multimedia Information Retrieval Aims and scope Submit manuscript

Abstract

Image recommendation is an important feature of search engine, as tremendous amount of images are available online. It is necessary to retrieve relevant images to meet the user’s requirement. In this paper, we present an algorithm image recommendation with absorbing Markov chain (IRAbMC) to retrieve relevant images for a user’s input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Keyword relevance is computed using absorbing Markov chain. Images are reranked using image visual features. Experimental results show that the IRAbMC algorithm outperforms Markovian semantic indexing (MSI) method with improved relevance score of retrieved ranked images.

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

Access this article

Log in via an institution

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Akbas E, Vural FTY (2007) Automatic image annotation by ensemble of visual descriptors. In: CVPR’07: the proceedings of IEEE conference on computer vision and pattern recognition, pp 1–8

  2. Bartolini I, Ciaccia P (2010) Multi-dimensional keyword-based image annotation and search. In: The Proceedings of the 2nd international workshop on keyword search on structured data, pp 5–10

  3. Wang C, Jing F, Zhang L, Zhang H-J (2007) Content-based image annotation refinement. In: CVPR’07: the proceedings of IEEE conference on computer vision and pattern recognition, pp 1–8

  4. Li J, Wang JZ (2008) Real-time computerized annotation of pictures. IEEE Trans Pattern Anal Mach Intell 30(6):985–1002

    Article  Google Scholar 

  5. Makadia A, Pavlovic V, Kumar S (2008) A new baseline for image annotation. Comput Vis ECCV 2008:316–329

    Google Scholar 

  6. Verma Y, Jawahar CV (2012) Image annotation using metric learning in semantic neighbourhoods. Comput Vis ECCV 2012:836–849

    Google Scholar 

  7. Wang C, Blei D, Li F-F (2009) Simultaneous image classification and annotation. In: CVPR 2009: the proceedings of IEEE conference on computer vision and pattern recognition, pp 1903–1910

  8. Guillaumin M, Mensink T, Verbeek J, Schmid C (2009) Tagprop: discriminative metric learning in nearest neighbor models for image auto-annotation. In: The proceedings of IEEE \(12^{th}\) international conference on computer vision, pp 309–316

  9. Stevenson K, Leung C (2005) Comparative evaluation of web image search engines for multimedia applications. In: ICME 2005: the proceedings of IEEE international conference on multimedia and expo, pp 4–14

  10. Smyth B (2007) A community-based approach to personalizing web search. IEEE J Comput 40(8):42–50

    Article  Google Scholar 

  11. He X, Cai D, Han J (2008) Learning a maximum margin subspace for image retrieval. IEEE Trans Knowl Data Eng 20(2):189–201

    Article  Google Scholar 

  12. Gao Y, Peng J, Luo H, Keim DA, Fan J (2009) An interactive approach for filtering out junk images from keyword-based google search results. IEEE Trans Circuits Syst Video Technol 19(12):1851–1865

    Article  Google Scholar 

  13. 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 

  14. Rahman MdM, Antani SK, Thoma GR (2011) A learning-based similarity fusion and filtering approach for biomedical image retrieval using SVM classification and relevance feedback. IEEE Trans Inf Technol Biomed 15(4):640–646

    Article  Google Scholar 

  15. Cheng E, Jing F, Zhang L (2009) A unified relevance feedback framework for web image retrieval. IEEE Trans Image Process 18(6):1350–1357

    Article  MathSciNet  Google Scholar 

  16. Kekre HB, Thepade SD, Mukherjee P, Wadhwa S, Kakaiya M, Singh S (2010) Image retrieval with shape features extracted using gradient operators and slope magnitude technique with BTC. Int J Comput Appl 6(8):28–33

  17. Guo J-M, Prasetyo H (2015) Content based image retrieval using features extracted from halftoning-based block truncation coding. IEEE Trans Image Process 24(3):1010–1024

    Article  MathSciNet  Google Scholar 

  18. Hofmann T (2001) Unsupervised learning by probabilistic latent semantic analysis. J Mach Learn 42(2):177–196

    Article  MATH  Google Scholar 

  19. Li Z, Tang Z, Zhao W, Li Z (2012) Combining generative/discriminative learning for automatic image annotation and retrieval. Int J Intell Sci 2(3):55–62

    Article  Google Scholar 

  20. Fan J, Gao Y, Luo H (2008) Integrating concept ontology and multitask learning to achieve more effective classifier training for multilevel image annotation. IEEE Trans Image Process 17(3):407–426

    Article  MathSciNet  Google Scholar 

  21. Vompras J, Scholz T, Conrad S (2008) Extracting contextual information from multiuser systems for improving annotation-based retrieval of image data. In: The proceedings of the 1st ACM international conference on multimedia information retrieval, pp 149–155

  22. OSullivan D, Wilson DC, Bertolotto M (2011) Task-based annotation and retrieval for image information management. Multimed Tools Appl 54(2):473–497

    Article  Google Scholar 

  23. Deniz Kılınç, Adil Alpkocak (2011) An expansion and reranking approach for annotation-based image retrieval from web. J Expert Syst Appl 38(10):13121–13127

    Article  Google Scholar 

  24. Wang X-J, Zhang L, Ma W-Y (2012) Duplicate search based image annotation using web scale data. IEEE J Electr Eng 100(9):2705–2721

    MathSciNet  Google Scholar 

  25. Riad A, Elminir H, Abd-Elghany S (2012) Web image retrieval search engine based on semantically shared annotation. Int J Comput Sci Issues 9(3):223–228

  26. Krishna AN, Prasad BG (2012) Automated image annotation for semantic indexing and retrieval of medical images. Int J Comput Appl 55(3):26–33

    Google Scholar 

  27. Ayadi MG, Bouslimi R, Akaichi J (2013) A new CBIR approach for the annotation of medical images. Int J Comput Appl 73(6):34–45

    Google Scholar 

  28. Zhang D, Islam MdM, Lu G (2013) Structural image retrieval using automatic image annotation and region based inverted file. J Visual Commun Image Represent 24(7):1087–1098

    Article  Google Scholar 

  29. Sang J, Changsheng X, Dongyuan L (2012) Learn to personalized image search from the photo sharing websites. IEEE Trans Multimed 14(4):963–974

    Article  Google Scholar 

  30. Li L-J, Fei-Fei L (2010) Optimol: automatic online picture collection via incremental model learning. Int J Comput Vis 88(2):147–168

    Article  Google Scholar 

  31. Pham T-T, Maillot NE, Lim J-H, Chevallet J-P (2007) Latent semantic fusion model for image retrieval and annotation. In: The proceedings of the sixteenth ACM conference on information and knowledge management, pp 439–444

  32. Song H, Li X, Wang P (2009) Multimodal image retrieval based on annotation keywords and visual content. In: CASE 2009: the proceedings of IEEE international conference on control, automation and systems engineering, pp 295–298

  33. Winston WL, Goldberg JB (2004) Operations research: applications and algorithms, vol 3. Duxbury press Belmont, CA, p 1440

  34. Raftopoulos KA, Ntalianis KS, Sourlas DD, Kollias SD (2013) Mining user queries with Markov chains: application to online image retrieval. IEEE Trans Knowl Data Eng 25(2):433–447

    Article  Google Scholar 

  35. Shapiro LG (2012) Ground truth database. Washington University. http://imagedatabase.cs.washington.edu/groundtruth/

  36. Berry MW, Dumais ST, O’Brien GW (1995) Using linear algebra for intelligent information retrieval. SIAM Rev 37(4):573–595

    Article  MathSciNet  MATH  Google Scholar 

  37. Salehian H, Zamani F, Jamzad M (2012) Fast content based color image retrieval system based on texture analysis of edge map. J Adv Mater Res 341:168–172

    Google Scholar 

  38. Lux M (2011) Content based image retrieval with LIRe. In: The proceedings of the \(19^{th}\) ACM international conference on multimedia, pp 735–738

  39. Huiskes MJ, Lew MS (2008) The MIR flickr retrieval evaluation. In: MIR ’08: proceedings of the 2008 ACM international conference on multimedia information retrieval

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bengaluru, 560001, India

    D. Sejal, V. Rashmi & K. R. Venugopal

  2. Florida International University, Miami, USA

    S. S. Iyengar

  3. National Institute of Advanced Studies, Bengaluru, India

    L. M. Patnaik

Authors
  1. D. Sejal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Rashmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. R. Venugopal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. S. Iyengar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. L. M. Patnaik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Sejal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sejal, D., Rashmi, V., Venugopal, K.R. et al. Image recommendation based on keyword relevance using absorbing Markov chain and image features. Int J Multimed Info Retr 5, 185–199 (2016). https://doi.org/10.1007/s13735-016-0104-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13735-016-0104-9

Keywords

Search

Navigation