Advertisement

Cognitive Computation

, Volume 10, Issue 1, pp 179–186 | Cite as

Reducing and Stretching Deep Convolutional Activation Features for Accurate Image Classification

  • Guoqiang Zhong
  • Shoujun Yan
  • Kaizhu Huang
  • Yajuan Cai
  • Junyu Dong
Article

Abstract

In order to extract effective representations of data using deep learning models, deep convolutional activation feature (DeCAF) is usually considered. However, since the deep models for learning DeCAF are generally pre-trained, the dimensionality of DeCAF is simply fixed to a constant number (e.g., 4096D). In this case, one may ask whether DeCAF is good enough for image classification and whether we can further improve its performance? In this paper, to answer these two challenging questions, we propose a new model called RS-DeCAF based on “reducing” and “stretching” the dimensionality of DeCAF. In the implementation of RS-DeCAF, we reduce the dimensionality of DeCAF using dimensionality reduction methods and increase its dimensionality by stretching the weight matrix between successive layers. To improve the performance of RS-DeCAF, we also present a modified version of RS-DeCAF by applying the fine-tuning operation. Extensive experiments on several image classification tasks show that RS-DeCAF not only improves DeCAF but also outperforms previous “stretching” approaches. More importantly, from the results, we find that RS-DeCAF can generally achieve the highest classification accuracy when its dimensionality is two to four times of that of DeCAF.

Keywords

Image classification Feature learning Deep convolutional neural network DeCAF Stretching 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61271405, 61403353), the Ph.D. Program Foundation of Ministry of Education Of China (No. 20120132110018) and the Fundamental Research Funds for the Central Universities of China.

Funding

This study was funded by the National Natural Science Foundation of China (No. 61271405, 61403353), the Ph.D. Program Foundation of Ministry of Education Of China (No. 20120132110018) and the Fundamental Research Funds for the Central Universities of China.

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Baudat G, Anouar F. Generalized discriminant analysis using a kernel approach. Neural Comput. 2000;12(10):2385–404.CrossRefPubMedGoogle Scholar
  2. 2.
    Brogaard B. An introduction to the philosophy of cognitive science. Mind Mach. 2002;12(1):151–6.CrossRefGoogle Scholar
  3. 3.
    Cai Y, Zhong G, Zheng Y, Huang K. Is DeCAF good enough for accurate image classification? ICONIP; 2015. p. 354–363.Google Scholar
  4. 4.
    Cho Y, Saul L. Large-margin classification in infinite neural networks. Neural Comput. 2010;22(10):2678–97.CrossRefPubMedGoogle Scholar
  5. 5.
    Coates A, Ng A, Lee H. An analysis of single-layer networks in unsupervised feature learning. In: AISTATS; 2011. p. 215–223.Google Scholar
  6. 6.
    Deng J, Dong W, Socher R, Li L, Li K, Li F. ImageNet: a large-scale hierarchical image database. In: CVPR; 2009. p. 248–255.Google Scholar
  7. 7.
    Donahue J, Jia Y, Vinyals O, Hoffman J, Zhang N, Tzeng E, Darrell T. DeCAF: a deep convolutional activation feature for generic visual recognition. In: ICML; 2014. p. 647–655.Google Scholar
  8. 8.
    Dosovitskiy A, Fischer P, Springenberg J, Riedmiller M, Brox T. Discriminative unsupervised feature learning with exemplar convolutional neural networks. IEEE Transactions on Pattern Analysis Machine Intelligence. 2016;38(9):1734–47.CrossRefPubMedGoogle Scholar
  9. 9.
    Fisher R. The use of multiple measurements in taxonomic problems. Annals of Eugenics. 1936;7(2):179–88.CrossRefGoogle Scholar
  10. 10.
    Gepperth A, Karaoguz CA. A bio-inspired incremental learning architecture for applied perceptual problems. Cognitive Computation. 2016;8(5):924–34.CrossRefGoogle Scholar
  11. 11.
    Guo T, Zhang L, Tan X. Neuron pruning-based discriminative extreme learning machine for pattern classification. Cognitive Computation. 2017Google Scholar
  12. 12.
    He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: CVPR; 2016. p. 770–778.Google Scholar
  13. 13.
    Hinton G, Osindero S, Teh Y. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18(7):1527–54.CrossRefPubMedGoogle Scholar
  14. 14.
    Hinton G, Salakhutdinov R. Reducing the dimensionality of data with neural networks. Science. 313. 2006.Google Scholar
  15. 15.
    Hinton H, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint. 2012;3:212–23.Google Scholar
  16. 16.
    Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T. Caffe: convolutional architecture for fast feature embedding. In: ACM MM; 2014. p. 675–678.Google Scholar
  17. 17.
    Jolliffe I. 1986. Principal component analysis. Springer.Google Scholar
  18. 18.
    Kelly J III. 2015. Computing, cognition and the future of knowing. IBM Research: Cognitive Computing.Google Scholar
  19. 19.
    Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. In: NIPS; 2012. p. 1106–1114.Google Scholar
  20. 20.
    LeCun Y, Boser B, Denker J, Henderson D, Howard R, Hubbard W, Jackel L. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1989;1(4):541–51.CrossRefGoogle Scholar
  21. 21.
    Lin M, Chen Q, Yan S. 2013. Network in network. CoRR arXiv:1312.4400.
  22. 22.
    Liu J, Dong J, Cai X, Qi L, Chantler M. 2015. Visual perception of procedural textures: identifying perceptual dimensions and predicting generation models. PloS One 10.Google Scholar
  23. 23.
    Luo B, Hussain A, Mahmud M, Tang J. Advances in brain-inspired cognitive systems. Cognitive Computation. 2016;8(5):795–6.Google Scholar
  24. 24.
    Netzer Y, Wang T, Coates A, Bissacco A, Wu B, Ng A. Reading digits in natural images with unsupervised feature learning . NIPS workshop on deep learning and unsupervised feature learning; 2011.Google Scholar
  25. 25.
    Pandey G, Dukkipati A. Learning by stretching deep networks. In: ICML; 2014. p. 1719–1727.Google Scholar
  26. 26.
    Peter W, Steve B, Takeshi M, Catherine W, Florian S, Serge B, Pietro P. Caltech-UCSD birds 200. Tech. Rep. CNS-TR-2010-001, California Institute of Technology. 2010Google Scholar
  27. 27.
    Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg A, Li F. ImageNet large scale visual recognition challenge. Int J Comput Vis. 2015;115(3):211–52.CrossRefGoogle Scholar
  28. 28.
    Scholkopf B, Smola A. Learning with kernels: support vector machines, regularization, optimization, and beyond. adaptive computation and machine learning series. MIT Press. 2002.Google Scholar
  29. 29.
    Scholkopf B, Smola A, Muller K. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 1998;10(5):1299–319.CrossRefGoogle Scholar
  30. 30.
    Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y. 2013. Overfeat: integrated recognition, localization and detection using convolutional networks eprint Arxiv.Google Scholar
  31. 31.
    Simonyan K, Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. CoRR arXiv:1409.1556.
  32. 32.
    Spratling M. A hierarchical predictive coding model of object recognition in natural images. Cognitive Computation. 2017;9(2):151–67.CrossRefPubMedGoogle Scholar
  33. 33.
    Sun Y, Wang X, Tang X. Deep learning face representation by joint Identification-Verification. NIPS; 2014. p. 1988–96.Google Scholar
  34. 34.
    Swersky K, Snoek J, Adams R. Multi-task bayesian optimization. NIPS; 2013. p. 2004–2012.Google Scholar
  35. 35.
    Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: CVPR; 2015. p. 1–9.Google Scholar
  36. 36.
    Taylor J. Cognitive computation. Cognitive Computation. 2009;1(1):4–16.CrossRefGoogle Scholar
  37. 37.
    Vapnik V. Statistical learning theory, vol. 1. Wiley. 1998.Google Scholar
  38. 38.
    Wang N, Yeung D. Ensemble-based tracking: Aggregating crowdsourced structured time series data. In: ICML; 2014. p. 1107–1115.Google Scholar
  39. 39.
    Yann L, Bottou L, Yoshua B, Patrick H. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86:2278–324.CrossRefGoogle Scholar
  40. 40.
    Zhang H, Ji P, Wang J, Chen X. A neutrosophic normal cloud and its application in decision-making. Cognitive Computation. 2016;8(4):649–69.CrossRefGoogle Scholar
  41. 41.
    Zheng Y, Zhong G, Liu J, Cai X, Dong J. Visual texture perception with feature learning models and deep architectures. In: CCPR; 2014. p. 401–410.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyOcean University of ChinaQingdaoChina
  2. 2.Department of Electrical and Electronic EngineeringXian Jiaotong-Liverpool UniversitySuzhouChina

Personalised recommendations