Advertisement

Image Tag Completion by Local Learning

  • Jingyan Wang
  • Yihua Zhou
  • Haoxiang Wang
  • Xiaohong Yang
  • Feng Yang
  • Austin Peterson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)

Abstract

The problem of tag completion is to learn the missing tags of an image. In this paper, we propose to learn a tag scoring vector for each image by 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 the 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.

Keywords

Image tagging Tag completion Local learning Gradient descent 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Feng, Z., Feng, S., Jin, R., Jain, A.K.: Image tag completion by noisy matrix recovery. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VII. LNCS, vol. 8695, pp. 424–438. Springer, Heidelberg (2014)Google Scholar
  2. 2.
    Gao, Y., Zhang, F., Bakos, J.D.: Sparse matrix-vector multiply on the keystone ii digital signal processor. In: 2014 IEEE High Performance Extreme Computing Conference (HPEC), pp. 1–6 (2014)Google Scholar
  3. 3.
    Hu, J., Zhang, F.: Improving protein localization prediction using amino acid group based physichemical encoding. In: Rajasekaran, S. (ed.) BICoB 2009. LNCS, vol. 5462, pp. 248–258. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Huang, S., Ma, Z., Wang, F.: A multi-objective design optimization strategy for vertical ground heat exchangers. Energy and Buildings 87, 233–242 (2015)CrossRefGoogle Scholar
  5. 5.
    Huang, Y., Liu, Q., Zhang, S., Metaxas, D.: Image retrieval via probabilistic hypergraph ranking. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3376–3383 (2010)Google Scholar
  6. 6.
    Li, L., Yang, J., Xu, Y., Qin, Z., Zhang, H.: Documents clustering based on max-correntropy nonnegative matrix factorization, pp. 850–855 (2015)Google Scholar
  7. 7.
    Li, T., Zhou, X., Brandstatter, K., Raicu, I.: Distributed key-value store on HPC and cloud systems. In: 2nd Greater Chicago Area System Research Workshop (GCASR) (2013)Google Scholar
  8. 8.
    Li, T., Zhou, X., Brandstatter, K., Zhao, D., Wang, K., Rajendran, A., Zhang, Z., Raicu, I.: Zht: A light-weight reliable persistent dynamic scalable zero-hop distributed hash table. In: 2013 IEEE 27th International Symposium on Parallel & Distributed Processing (IPDPS), pp. 775–787 (2013)Google Scholar
  9. 9.
    Li, Z., Liu, J., Xu, C., Lu, H.: Mlrank: Multi-correlation learning to rank for image annotation. Pattern Recognition 46(10), 2700–2710 (2013)CrossRefGoogle Scholar
  10. 10.
    Lin, Z., Ding, G., Hu, M., Lin, Y., Sam Ge, S.: Image tag completion via dual-view linear sparse reconstructions. Computer Vision and Image Understanding 124, 42–60 (2014)CrossRefGoogle Scholar
  11. 11.
    Lin, Z., Ding, G., Hu, M., Wang, J., Ye, X.: 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 (2013)Google Scholar
  12. 12.
    Liu, L., Li, H., Xue, Y., Liu, W.: Reactive power compensation and optimization strategy for grid-interactive cascaded photovoltaic systems. IEEE Transactions on Power Electronics 30(1), 188–202 (2015)CrossRefGoogle Scholar
  13. 13.
    Liu, Z., Abbas, A., Jing, B.Y., Gao, X.: Wavpeak: picking nmr peaks through wavelet-based smoothing and volume-based filtering. Bioinformatics 28(7), 914–920 (2012)CrossRefGoogle Scholar
  14. 14.
    Wang, C., Yan, S., Zhang, L., Zhang, H.J.: Multi-label sparse coding for automatic image annotation, pp. 1643–1650 (2009)Google Scholar
  15. 15.
    Wang, J.J.Y., Bensmail, H., Gao, X.: Multiple graph regularized protein domain ranking. BMC Bioinformatics 13(1), 307 (2012)CrossRefGoogle Scholar
  16. 16.
    Wang, J.J.Y., Bensmail, H., Gao, X.: Multiple graph regularized nonnegative matrix factorization. Pattern Recognition 46(10), 2840–2847 (2013)CrossRefGoogle Scholar
  17. 17.
    Wang, J.J.Y., Wang, X., Gao, X.: Non-negative matrix factorization by maximizing correntropy for cancer clustering. BMC Bioinformatics 14(1), 107 (2013)CrossRefGoogle Scholar
  18. 18.
    Wang, K., Kulkarni, A., Zhou, X., Lang, M., Raicu, I.: Using simulation to explore distributed key-value stores for exascale system services. In: 2nd Greater Chicago Area System Research Workshop (GCASR) (2013)Google Scholar
  19. 19.
    Wang, K., Zhou, X., Chen, H., Lang, M., Raicu, I.: Next generation job management systems for extreme-scale ensemble computing. In: Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing, pp. 111–114 (2014)Google Scholar
  20. 20.
    Wang, K., Zhou, X., Li, T., Zhao, D., Lang, M., Raicu, I.: Optimizing load balancing and data-locality with data-aware scheduling. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 119–128 (2014)Google Scholar
  21. 21.
    Wang, K., Zhou, X., Qiao, K., Lang, M., McClelland, B., Raicu, I.: Towards scalable distributed workload manager with monitoring-based weakly consistent resource stealing. In: Proceedings of the 24rd International Symposium on High-Performance Parallel and Distributed Computing, pp. 219–222 (2015)Google Scholar
  22. 22.
    Wu, L., Jin, R., Jain, A.: Tag completion for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3), 716–727 (2013)CrossRefGoogle Scholar
  23. 23.
    Xia, Z., Feng, X., Peng, J., Wu, J., Fan, J.: A regularized optimization framework for tag completion and image retrieval. Neurocomputing (2014)Google Scholar
  24. 24.
    Xing, H.J., Ren, H.R.: Regularized correntropy criterion based feature extraction for novelty detection. Neurocomputing 133, 483–490 (2014)CrossRefGoogle Scholar
  25. 25.
    Zhang, F., Gao, Y., Bakos, J.D.: Lucas-kanade optical flow estimation on the ti c66x digital signal processor. In: 2014 IEEE High Performance Extreme Computing Conference (HPEC), pp. 1–6 (2014)Google Scholar
  26. 26.
    Zhang, F., Hu, J.: Bayesian classifier for anchored protein sorting discovery. In: IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009, pp. 424–428 (2009)Google Scholar
  27. 27.
    Zhang, F., Hu, J.: Bioinformatics analysis of physicochemical properties of protein sorting signals (2010)Google Scholar
  28. 28.
    Zhang, F., Zhang, Y., Bakos, J.: Gpapriori: Gpu-accelerated frequent itemset mining. In: 2011 IEEE International Conference on Cluster Computing (CLUSTER), pp. 590–594 (2011)Google Scholar
  29. 29.
    Zhang, F., Zhang, Y., Bakos, J.D.: Accelerating frequent itemset mining on graphics processing units. The Journal of Supercomputing 66(1), 94–117 (2013)CrossRefGoogle Scholar
  30. 30.
    Zhang, S., Huang, J., Li, H., Metaxas, D.: Automatic image annotation and retrieval using group sparsity. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42(3), 838–849 (2012)CrossRefGoogle Scholar
  31. 31.
    Zhang, X., Liu, C.: Image annotation based on feature fusion and semantic similarity. Neurocomputing 149(PC), 1658–1671 (2015)Google Scholar
  32. 32.
    Zhang, Y., Zhang, F., Bakos, J.: Frequent itemset mining on large-scale shared memory machines. In: 2011 IEEE International Conference on Cluster Computing (CLUSTER), pp. 585–589 (2011)Google Scholar
  33. 33.
    Zhang, Y., Zhang, F., Jin, Z., Bakos, J.D.: An fpga-based accelerator for frequent itemset mining. ACM Transactions on Reconfigurable Technology and Systems (TRETS) 6(1), 2 (2013)Google Scholar
  34. 34.
    Zhang, Z., Chen, J.: Correntropy based data reconciliation and gross error detection and identification for nonlinear dynamic processes. Computers and Chemical Engineering 75, 120–134 (2015)CrossRefGoogle Scholar
  35. 35.
    Zhao, D., Zhang, Z., Zhou, X., Li, T., Wang, K., Kimpe, D., Carns, P., Ross, R., Raicu, I.: Fusionfs: Toward supporting data-intensive scientific applications on extreme-scale high-performance computing systems. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 61–70 (2014)Google Scholar
  36. 36.
    Zhou, X., Chen, H., Wang, K., Lang, M., Raicu, I.: Exploring distributed resource allocation techniques in the slurm job management system. Illinois Institute of Technology, Department of Computer Science, Technical Report (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

<SimplePara><Emphasis Type="Bold">Open Access</Emphasis> This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. </SimplePara> <SimplePara>The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.</SimplePara>

Authors and Affiliations

  • Jingyan Wang
    • 1
    • 2
    • 3
  • Yihua Zhou
    • 4
  • Haoxiang Wang
    • 5
  • Xiaohong Yang
    • 6
  • Feng Yang
    • 6
  • Austin Peterson
    • 7
  1. 1.National Time Service CenterChinese Academy of SciencesXi’anChina
  2. 2.Graduate University of Chinese Academy of SciencesBeijingChina
  3. 3.Provincial Key Laboratory for Computer Information Processing TechnologySoochow UniversitySuzhouChina
  4. 4.Department of Mechanical Engineering and MechanicsLehigh UniversityBethlehemUS
  5. 5.Department of Electrical and Computer EngineeringCornell UniversityIthacaUSA
  6. 6.College of Computer Science and TechnologyShandong University of Finance and EconomicsJinanChina
  7. 7.Electrical and Computer Engineering DepartmentThe University of Texas at San AntonioSan AntonioUSA

Personalised recommendations