TIAN Feng and SHEN Xukun, “Learning Semantic Concepts from Noisy Media Collection for Automatic Image Annotation,” Chinese Journal of Electronics, vol. 24, no. 4, pp. 790-794, 2015, doi: 10.1049/cje.2015.10.021
Citation: TIAN Feng and SHEN Xukun, “Learning Semantic Concepts from Noisy Media Collection for Automatic Image Annotation,” Chinese Journal of Electronics, vol. 24, no. 4, pp. 790-794, 2015, doi: 10.1049/cje.2015.10.021

Learning Semantic Concepts from Noisy Media Collection for Automatic Image Annotation

doi: 10.1049/cje.2015.10.021
Funds:  This work is supported by the Scientific Research Fund of Heilongjiang Provincial Education Department (No.12521055) and the Youth Foundation of Northeast Petroleum University (No.2013NQ120, No.2012QN117).
  • Received Date: 2013-01-04
  • Rev Recd Date: 2014-08-18
  • Publish Date: 2015-10-10
  • Along with the explosive growth of images, automatic image annotation has attracted great interest of various research communities. However, despite the great progress achieved in the past two decades, automatic annotation is still an important open problem in computer vision, and can hardly achieve satisfactory performance in real-world environment. In this paper, we address the problem of annotation when noise is interfering with the dataset. A semantic neighborhood learning model on noisy media collection is proposed. Missing labels are replenished, and semantic balanced neighborhood is construct. The model allows the integration of multiple label metric learning and local nonnegative sparse coding. We construct semantic consistent neighborhood for each sample, thus corresponding neighbors have higher global similarity, partial correlation, conceptual similarity along with semantic balance. Meanwhile, an iterative denoising method is also proposed. The method proposed makes a marked improvement as compared to the current state-of-the-art.
  • loading
  • M. Wang, B. Ni and X.S. Hua, "Assistive tagging: A survey of multimedia tagging with human-computer joint exploration", ACM Computing Surveys(CSUR), Vol.44, No.4, pp.25- 25, 2012.
    J. Liu and J.P. Du, "Latent topic fusion-based cross-media image semantic annotation", Acta Electronica Sinica, Vol.42, No.5, pp.987-991, 2014.
    A. Makadia, V. Pavlovic and S. Kumar, "A new baseline for image annotation", Proc. of ECCV 2008, Marseille, France, pp.316-329, 2009.
    M. Guillaumin, T. Mensink and J. Verbeek, "Tagprop: Discriminative metric learning in nearest neighbor models for image auto-annotation", Proc. of IEEE 12th International Conference on Computer Vision, Miami, Florida, USA, pp.309-316, 2009.
    S. Zhang, J. Huang and Y. Huang, "Automatic image annotation using group sparsity", Proc. of 23th IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA, pp.3312-3319, 2010.
    N. Nguyen and R. Caruana, "Classification with partial labels", Proc. of 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, USA, pp.551-559, 2008.
    J. Fan, Y. Shen and N. Zhou, "Harvesting large-scale weaklytagged image databases from the web", Proc. of 23th IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA, pp.802-809, 2010.
    X. He and R.S. Zemel, "Learning hybrid models for image annotation with partially labeled data", Proc. of 24th Annual Conference on Advances in Neural Information Processing Systems, Vancouver, Canada, pp.625-632, 2009.
    S.S. Bucak, R. Jin and A.K. Jain, "Multi-label learning with incomplete class assignments", Proc. of 24th IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, USA, pp.2801-2808, 2011.
    S. Shalev-Shwartz, Y. Singer and N. Srebro, "Pegasos: Primal estimated sub-gradient solver for SVM", Mathematical programming, Vol.127, No.1, pp.3-30, 2011.
    Y. Saad and M.H. Schultz, "GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems", SIAM Journal on scientific and statistical computing, Vol.7, No.3, pp.856-569, 1986.
    M.J. Huiskes and M.S. Lew, "The MIR flickr retrieval evaluation", Proc. of 1st ACM International Conference on Multimedia Information Retrieval, Vancouver, Canada, pp.39-43, 2008.
    G. Carneiro, A.B. Chan and P.J. Moreno, "Supervised learning of semantic classes for image annotation and retrieval", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.29, No.3, pp.394-410, 2007.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (410) PDF downloads(539) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return