Skip to main content
SpringerLink
Log in

Robust discriminative extreme learning machine for relevance feedback in image retrieval

  • Published:
Multidimensional Systems and Signal Processing Aims and scope Submit manuscript

Abstract

Relevance feedback (RF) has long been an important approach for multi-media retrieval because of the semantic gap in image content, where SVM based methods are widely applied to RF of content-based image retrieval. However, RF based on SVM still has some limitations: (1) the high dimension of image features always make the RF time-consuming; (2) the model of SVM is not discriminative, because labels of image features are not sufficiently exploited. To solve above problems, we proposed robust discriminative extreme learning machine (RDELM) in this paper. RDELM involved both robust within-class and between-class scatter matrices to enhance the discrimination capacity of ELM for RF. Furthermore, an angle criterion dimensionality reduction method is utilized to extract the discriminative information for RDELM. Experimental results on four benchmark datasets (Corel-1K, Corel-5K, Corel-10K and MSRC) illustrate that our proposed RF method in this paper achieves better performance than several state-of-the-art methods.

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

Similar content being viewed by others

References

  • Akusok, A., Miche, Y., Karhunen, J., et al. (2015). Arbitrary category classification of websites based on image content. IEEE on Computational Intelligence Magazine, 10(2), 30–41.

    Article  Google Scholar 

  • Anitha, S., & Rinesh, S. (2013). Semi-supervised biased maximum margin analysis for interactive image retrieval. Research Journal of Computer Systems Engineering, 4, 532–536.

    Google Scholar 

  • Cao, J., Huang, W., Zhao, T., Wang, J., & Wang, R. (2015a). An enhance excavation equipments classification algorithm based on acoustic spectrum dynamic feature. Multidimensional Systems and Signal Processing. doi:10.1007/s11045-015-0374-z.

  • Cao, J., & Lin, Z. (2015). Extreme learning machine on high dimensional and large data applications: A survey. Mathematical Problems in Engineering. doi:10.1155/2015/103796.

  • Cao, J., Lin, Z., Huang, G.-B., & Liu, N. (2012). Voting based extreme learning machine. Information Sciences, 185(1), 66–77.

    Article  MathSciNet  Google Scholar 

  • Cao, J., Zhao, Y., Lai, X., Ong, M., Yin, C., Koh, Z., et al. (2015b). Landmark recognition with sparse representation classification and extreme learning machine. Journal of The Franklin Institute, 352(10), 4528–4545.

    Article  MathSciNet  Google Scholar 

  • Deng, W., Zheng, Q., & Chen, L. (2009). Regularized extreme learning machine. In Computational intelligence and data mining, CIDM’09 (pp. 389–395).

  • Feng, L., Liu, S., Xiao, Y., et al. (2015). A novel CBIR system with WLLTSA and ULRGA. Neurocomputing, 147, 509–522.

    Article  Google Scholar 

  • He, X. (2004). Incremental semi-supervised subspace learning for image retrieval. In Proceedings of the 12th annual ACM international conference on multimedia (pp. 2–8).

  • He, X., & Niyogi, P. (2003). Locality preserving projections. In Advances in neural information processing systems 16. Vancouver, Canada.

  • He, Q., Jin, X., Du, C., et al. (2014). Clustering in extreme learning machine feature space. Neurocomputing, 128, 88–95.

    Article  Google Scholar 

  • Hoi, S. C. H., Jin, R., Zhu, J., et al. (2008) Semi-supervised SVM batch mode active learning for image retrieval. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 1–7).

  • Hoi, S. C. H., & Lyu, M. R. (2005). A semi-supervised active learning framework for image retrieval. Computer Vision and Pattern Recognition, 2, 302–309.

    Google Scholar 

  • Horata, P., Chiewchanwattana, S., & Sunat, K. (2013). Robust extreme learning machine. Neurocomputing, 102, 31–44.

    Article  Google Scholar 

  • Huang, G.-B. (2015). What are extreme learning machines? Filling the gap between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle. Cognitive Computation, 7, 263–278.

    Article  Google Scholar 

  • Huang, G., & Chen, L. (2007). Convex incremental extreme learning machine. Neurocomputing, 70(16–18), 3056–3062.

    Article  Google Scholar 

  • Huang, G., Chen, L., & Siew, C.-K. (2006). Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks, 17(4), 879–892.

    Article  Google Scholar 

  • Huang, G. B., Zhou, H., Ding, X., et al. (2012). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42(2), 513–529.

    Article  Google Scholar 

  • Iosifidis, A., Tefas, A., & Pitas, I. (2013). Minimum class variance extreme learning machine for human action recognition. IEEE Transactions on Circuits and Systems for Video Technology, 23(11), 1968–1979.

    Article  Google Scholar 

  • Iosifidis, A., Tefas, A., & Pitas, I. (2014). Regularized extreme learning machine for multi-view semi-supervised action recognition. Neurocomputing, 145, 250–262.

    Article  Google Scholar 

  • Jin, Y., Cao, J., Wang, Y., et al. (2015). Ensemble based extreme learning machine for cross-modality face matching. Multimedia Tools and Applications, 1–16.

  • Kundu, M. K., Chowdhury, M., & Bulò, S. R. (2015). A graph-based relevance feedback mechanism in content-based image retrieval. Knowledge-Based Systems, 73, 254–264.

    Article  Google Scholar 

  • Liu, S., Feng, L., & Qiao, H. (2015). Scatter Balance: An angle-based supervised dimensionality reduction. IEEE Transactions on Neural Networks and Learning Systems, 26(2), 277–289.

    Article  MathSciNet  Google Scholar 

  • Liu, S., Feng, L., Xiao, Y., et al. (2014). Robust activation function and its application: Semi-supervised kernel extreme learning method. Neurocomputing, 144, 318–328.

    Article  Google Scholar 

  • Liu, G. H., Li, Z. Y., Zhang, L., et al. (2011). Image retrieval based on micro-structure descriptor. Pattern Recognition, 44(9), 2123–2133.

    Article  Google Scholar 

  • Lu, K., Zhao, J., & Cai, D. (2006). An algorithm for semi-supervised learning in image retrieval. Pattern Recogition, 39(4), 717–720.

    Article  MATH  Google Scholar 

  • Minhas, R., Baradarani, A., Seifzadeh, S., et al. (2010). Human action recognition using extreme learning machine based on visual vocabularies. Neurocomputing, 73(10), 1906–1917.

    Article  Google Scholar 

  • Mohammed, A. A., Minhas, R., Wu, Q. M. J., et al. (2011). Human face recognition based on multidimensional PCA and extreme learning machine. Pattern Recognition, 44(10), 2588–2597.

    Article  MATH  Google Scholar 

  • Murala, S., & Wu, Q. M. (2014). Local mesh patterns versus local binary patterns: Biomedical image indexing and retrieval. IEEE Journal of Biomedical and Health Informatics, 18(3), 929–938.

    Article  Google Scholar 

  • Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987.

    Article  MATH  Google Scholar 

  • Swain, M. J., & Ballard, D. H. (1991). Color indexing. International Journal of Computer Vision, 7(1), 11–32.

    Article  Google Scholar 

  • Tan, X., & Triggs, B. (2007). Enhanced local texture feature sets for face recognition under difficult lighting conditions. In Analysis and modeling of faces and gestures (pp. 168–182). Berlin: Springer.

  • Tang, X., & Han, M. (2009). Partial Lanczos extreme learning machine for single-output regression problems. Neurocomputing, 72(13–15), 3066–3076.

    Article  Google Scholar 

  • Tang, X., & Han, M. (2009). Partial Lanczos extreme learning machine for single-output regression problems. Neurocomputing, 72(13–15), 3066–3076.

    Article  Google Scholar 

  • Zhang, P., & Yang, Z. (2015). A robust AdaBoost.RT based ensemble extreme learning machine. Mathematical Problems in Engineering, 2015, 260970. http://www.hindawi.com/journals/mpe/2015/260970/cta/.

  • Zhang, K., & Luo, M. (2015). Outlier-robust extreme learning machine for regression problems. Neurocomputing, 151, 1519–1527.

    Article  Google Scholar 

  • Zhang, S., Yang, M., Cour, T., et al. (2015). Query specific rank fusion for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(4), 803–815.

Download references

Author information

Authors and Affiliations

  1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China

    Shenglan Liu, Lin Feng, Yang Liu, Jun Wu, MuXin Sun & Wei Wang

  2. School of Innovation Experiment, Dalian University of Technology, Dalian, 116024, China

    Shenglan Liu, Lin Feng, Yang Liu, Jun Wu, MuXin Sun & Wei Wang

Authors
  1. Shenglan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lin Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jun Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. MuXin Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lin Feng.

Additional information

This work was supported by National Natural Science Foundation of P. R. China (61173163, 61370200) and China Postdoctoral Science Foundation (ZX20150629).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, S., Feng, L., Liu, Y. et al. Robust discriminative extreme learning machine for relevance feedback in image retrieval. Multidim Syst Sign Process 28, 1071–1089 (2017). https://doi.org/10.1007/s11045-016-0386-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11045-016-0386-3

Keywords

Search

Navigation