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A Feature Learning Approach for Image Retrieval

  • Junfeng Yao
  • Yao Yu
  • Yukai Deng
  • Changyin SunEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)

Abstract

Extraction of effective image features is the key to the content-based image retrieval task. Recently, deep convolutional neural networks have been widely used in learning image features and have achieved top results. Based on CNNs, metric learning methods like contrastive loss and triplet loss have been proved effective in learning discriminative image features. In this paper, we propose a new supervised signal to train convolutional neural networks. This step could ensure that the features obtained are well differentiated in space, which is very suitable for image retrieval task. We give an example on MNIST to illustrate the intent of this loss function. Also, we evaluate our method on two datasets including CUB-200-2011, CARS196. The experimental results show that the retrieval effect is fairly good on this two datasets. Besides, our loss function is much easier to implement and train.

Keywords

Image retrieval Convolutional neural networks Metric learning 

References

  1. 1.
    Lowe, D.G.: Distinctive image features from scale-invariant key points. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  2. 2.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  3. 3.
    Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: 26th International Conference on Neural Information Processing Systems, pp. 1097–1105. Curran Associates Inc., Harrahs and Harveys, Lake Tahoe (2012)Google Scholar
  4. 4.
    Szegedy, C., Liu, W., Jia, Y.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9. IEEE Computer Society, Boston (2015)Google Scholar
  5. 5.
    He, K., Zhang, X., Ren, S.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE Computer Society, Las Vegas (2016)Google Scholar
  6. 6.
    Bell, S., Bala, K.: Learning visual similarity for product design with convolutional neural networks. ACM Trans. Graph. 34(4), 98 (2015)CrossRefGoogle Scholar
  7. 7.
    Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823. IEEE Computer Society, Boston (2015)Google Scholar
  8. 8.
    Song, H.O., Xiang, Y., Jegelka, S., Savarese, S.: Deep metric learning via lifted structured feature embedding. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4004–4012. IEEE Computer Society, Las Vegas (2016)Google Scholar
  9. 9.
    Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891–1898. IEEE Computer Society, Columbus (2014)Google Scholar
  10. 10.
    Sun, Y., Chen, Y., Wang, X., Tang, X.: Deep learning face representation by joint identification-verification. In: 24th International Conference on Neural Information Processing Systems, pp. 107–114. Curran Associates Inc., Montréal (2014)Google Scholar
  11. 11.
    Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: British Machine Vision Conference, pp. 41.1–41.12. BMVC, Swansea (2015)Google Scholar
  12. 12.
    Wen, Y., Zhang, K., Li, Z., Qiao, Yu.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). doi: 10.1007/978-3-319-46478-7_31 Google Scholar
  13. 13.
  14. 14.
    Russakovsky, O., Deng, J., Su, H.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Sanderson, M., Christopher, D.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2010)Google Scholar
  16. 16.
    Jegou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 117 (2011)CrossRefGoogle Scholar
  17. 17.
  18. 18.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijingChina
  2. 2.School of AutomationSoutheast UniversityNaijingChina

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