Skip to main content
SpringerLink
Log in

Completing tags by local learning: a novel image tag completion method based on neighborhood tag vector predictor

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In this paper, we study the problem of tag completion. Given an image and a set of tags, only a few of the tags are known to be associated with this image or not, and the problem is to predict whether the other tags are associated with the image. To solve this problem, we propose to learn a tag scoring vector for each image and use it to predict the associated tags of the image. To learn the tag scoring vector, we use the method of local linear learning. A local linear function is used in the neighborhood of each image to predict the tag scoring vectors of its neighboring images. We construct a unified objective function for the learning of both tag scoring vectors and local linear function parameters. In this objective, we impose the learned tag scoring vectors to be consistent with the known associations to the tags of each image and also minimize the prediction error of each local linear function, while reducing the complexity of each local function. The objective function is optimized by an alternate optimization strategy and gradient descent methods in an iterative algorithm. We compare the proposed algorithm against different state-of-the-art tag completion methods, and the results show its advantages.

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

Similar content being viewed by others

References

  1. Bu Z, Zhang C, Xia Z, Wang J (2013) A fast parallel modularity optimization algorithm (fpmqa) for community detection in online social network. Knowl Based Syst 50:246–259

    Article  Google Scholar 

  2. Cervellera C, MacCio D (2014) Local linear regression for function learning: an analysis based on sample discrepancy. IEEE Trans Neural Netw Learn Syst 25(11):2086–2098

    Article  Google Scholar 

  3. Cervellera C, Maccio D, Marcialis R (2013) Function learning with local linear regression models: an analysis based on discrepancy. In: Proceedings of the international joint conference on neural networks

  4. Cheng MY, Wu HT (2013) Local linear regression on manifolds and its geometric interpretation. J Am Stat Assoc 108(504):1421–1434

    Article  MathSciNet  MATH  Google Scholar 

  5. Cormack G, Lynam T (2006) Statistical precision of information retrieval evaluation. In: Proceedings of the twenty-ninth annual international ACM SIGIR conference on research and development in information retrieval, vol. 2006, pp 533–540

  6. Dai L, Gao X, Guo Y, Xiao J, Zhang Z et al (2012) Bioinformatics clouds for big data manipulation. Biol Direct 7(1):43

    Article  Google Scholar 

  7. Feng Z, Feng S, Jin R, Jain A (2014) Image tag completion by noisy matrix recovery. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) 8695 LNCS(PART 7), pp 424–438

  8. Font F, Serrá J, Serra X (2014) Class-based tag recommendation and user-based evaluation in online audio clip sharing. Knowl Based Syst 67:131–142

    Article  Google Scholar 

  9. Huang CL, Yeh PH, Lin CW, Wu DC (2014) Utilizing user tag-based interests in recommender systems for social resource sharing websites. Knowl Based Syst 56:86–96

    Article  Google Scholar 

  10. Huang S, Ma Z, Wang F (2015) A multi-objective design optimization strategy for vertical ground heat exchangers. Energy Build 87:233–242

    Article  Google Scholar 

  11. Huang Y, Liu Q, Zhang S, Metaxas D (2010) Image retrieval via probabilistic hypergraph ranking. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 3376–3383

  12. Jaber A, Ismail M (2014) Comparison between empirical mode decomposition and local linear quantile regression in the presence of boundaries. In: AIP conference proceedings vol. 1605, pp 1046–1050

  13. Ji K, Shen H (2014) Addressing cold-start: scalable recommendation with tags and keywords. Knowl Based Syst 83:42–50. doi:10.1016/j.knosys.2015.03.008

    Article  Google Scholar 

  14. Jiang R, Qiao H, Zhang B (2015) Speeding up graph regularized sparse coding by dual gradient ascent. IEEE Signal Process Lett 22(3):313–317

    Article  Google Scholar 

  15. Kim I, Lee CH (2011) Optimization of average precision with maximal figure-of-merit learning. In: IEEE international workshop on machine learning for signal processing, p 6064638

  16. Lee H, Hoo W, Chan C (2015) Color video denoising using epitome and sparse coding. Expert Syst Appl 42(2):751–759

    Article  Google Scholar 

  17. Li K, Huang Z, Cheng YC, Lee CH (2014) A maximal figure-of-merit learning approach to maximizing mean average precision with deep neural network based classifiers. In: ICASSP, IEEE international conference on acoustics, speech and signal processing—proceedings, pp 4503–4507

  18. Li L, Yang J, Xu Y, Qin Z, Zhang H (2015) Documents clustering based on max-correntropy nonnegative matrix factorization. In: Proceedings—international conference on machine learning and cybernetics vol. 2, pp 850–855

  19. Li Z, Liu J, Xu C, Lu H (2013) Mlrank: multi-correlation learning to rank for image annotation. Pattern Recognit 46(10):2700–2710

    Article  MATH  Google Scholar 

  20. Lin Z, Ding G, Hu M, Lin Y, Sam Ge S (2014) Image tag completion via dual-view linear sparse reconstructions. Comput Vis Image Underst 124:42–60

    Article  Google Scholar 

  21. Lin Z, Ding G, Hu M, Wang J, Ye X (2013) Image tag completion via image-specific and tag-specific linear sparse reconstructions. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 1618–1625

  22. Liu L, Li H, Xue Y, Liu W (2015) Reactive power compensation and optimization strategy for grid-interactive cascaded photovoltaic systems. IEEE Trans Power Electron 30(1):188–202

    Article  Google Scholar 

  23. Liu Z, Abbas A, Jing BY, Gao X (2012) Wavpeak: picking nmr peaks through wavelet-based smoothing and volume-based filtering. Bioinformatics 28(7):914–920

    Article  Google Scholar 

  24. Marés J, Torra V (2013) On the protection of social networks user’s information. Knowl Based Syst 49:134–144

    Article  Google Scholar 

  25. Quijano-Sánchez L, Díaz-Agudo B, Recio-García J (2014) Development of a group recommender application in a social network. Knowl Based Syst 71:72–85

    Article  Google Scholar 

  26. Sajjad M, Mehmood I, Baik S (2015) Image super-resolution using sparse coding over redundant dictionary based on effective image representations. J Vis Commun Image Represent 26:50–65

    Article  Google Scholar 

  27. Wang C, Yan S, Zhang L, Zhang HJ (2009) Multi-label sparse coding for automatic image annotation. pp 1643–1650

  28. Wang J, Gao X, Wang Q, Li Y (2013) Prodis-contshc: learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrieval. BMC Bioinform 13(suppl. 7)

  29. Wang JJY, Bensmail H, Gao X (2012) Multiple graph regularized protein domain ranking. BMC Bioinform 13(1):307

    Article  Google Scholar 

  30. Wang JJY, Bensmail H, Gao X (2013) Joint learning and weighting of visual vocabulary for bag-of-feature based tissue classification. Pattern Recognit 46(12):3249–3255

    Article  Google Scholar 

  31. Wang JJY, Bensmail H, Gao X (2013) Multiple graph regularized nonnegative matrix factorization. Pattern Recognit 46(10):2840–2847

    Article  MATH  Google Scholar 

  32. Wang JJY, Wang X, Gao X (2013) Non-negative matrix factorization by maximizing correntropy for cancer clustering. BMC Bioinform 14(1):107

    Article  Google Scholar 

  33. Wang JY, Almasri I, Gao X (2012) Adaptive graph regularized nonnegative matrix factorization via feature selection. In: Proceedings—international conference on pattern recognition, pp 963–966

  34. Wu J, Chiclana F (2014) A social network analysis trust-consensus based approach to group decision-making problems with interval-valued fuzzy reciprocal preference relations. Knowl Based Syst 59:97–107

    Article  Google Scholar 

  35. Wu L, Jin R, Jain A (2013) Tag completion for image retrieval. IEEE Trans Pattern Anal Mach Intell 35(3):716–727

    Article  Google Scholar 

  36. Wu M, Schölkopf B (2007) A local learning approach for clustering. In: advances in neural information processing systems, pp 1529–1536

  37. Xia Z, Feng X, Peng J, Wu J, Fan J (2014) A regularized optimization framework for tag completion and image retrieval. Neurocomputing

  38. Xing HJ, Ren HR (2014) Regularized correntropy criterion based feature extraction for novelty detection. Neurocomputing 133:483–490

    Article  Google Scholar 

  39. Yang Y, Nie F, Xu D, Luo J, Zhuang Y, Pan Y (2012) A multimedia retrieval framework based on semi-supervised ranking and relevance feedback. IEEE Trans Pattern Anal Mach Intell 34(4):723–742

    Article  Google Scholar 

  40. Yang Y, Xu D, Nie F, Luo J, Zhuang Y (2009) Ranking with local regression and global alignment for cross media retrieval. pp 175–184

  41. Yue Y, Finley T, Radlinski F, Joachims T (2007) A support vector method for optimizing average precision. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, SIGIR’07, pp 271–278

  42. Zhang H, Yu J, Wang M, Liu Y (2012) Semi-supervised distance metric learning based on local linear regression for data clustering. Neurocomputing 93:100–105

    Article  Google Scholar 

  43. Zhang S, Huang J, Li H, Metaxas D (2012) Automatic image annotation and retrieval using group sparsity. IEEE Trans Syst, Man, Cybern, Part B Cybern 42(3):838–849

    Article  Google Scholar 

  44. Zhang X, Liu C (2015) Image annotation based on feature fusion and semantic similarity. Neurocomputing 149(PC): pp 1658–1671

  45. Zhang Z, Chen J (2015) Correntropy based data reconciliation and gross error detection and identification for nonlinear dynamic processes. Comput Chem Eng 75:120–134

    Article  Google Scholar 

  46. Znaidia A, Le Borgne H, Hudelot C (2013) Tag completion based on belief theory and neighbor voting. In: ICMR 2013—Proceedings of the 3rd ACM international conference on multimedia retrieval, pp 49–56

Download references

Acknowledgments

This project is supported by National Natural Science Foundation of China (Grant No. 71172046).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, China

    Xiaohong Yang & Feng Yang

Authors
  1. Xiaohong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feng Yang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, X., Yang, F. Completing tags by local learning: a novel image tag completion method based on neighborhood tag vector predictor. Neural Comput & Applic 27, 2407–2416 (2016). https://doi.org/10.1007/s00521-015-1983-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-015-1983-z

Keywords

Search

Navigation