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Compact Deep Neural Networks for Device-Based Image Classification

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Mobile Cloud Visual Media Computing

Abstract

Convolutional Neural Network (CNN) is efficient in learning hierarchical features from large image datasets, but its model complexity and large memory footprints prevent it from being deployed to devices without a server back-end support. Modern CNNs are always trained on GPUs or even GPU clusters with high-speed computation capability due to the immense size of the network. A device-based deep learning CNN engine for image classification can be very useful for situations where server back end is either not available, or its communication link is weak and unreliable. Methods on regulating the size of the network, on the other hand, are rarely studied. In this chapter we present a novel compact architecture that minimizes the number and complexity of lower level filters in a CNN by separating the color information from the original image. A 9-patch histogram extractor is built to exploit the unused color information. A high-level classifier is then used to learn the features obtained from the compact CNN that was trained only on grayscale image with limited number of filters and the 9-patch histogram extracted from the color information in the image. We apply our compact architecture to Samsung Mobile Image Dataset for image classification. The proposed solution has a recognition accuracy on par with the state-of-the-art CNNs, while achieving significant reduction in model memory footprint. With these advantages, our system is being deployed to the mobile devices.

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References

  1. Chapelle, O., Haffner, P., Vapnik, V.N.: Support vector machines for histogram-based image classification. IEEE Trans. Neural Netw. 10(5), 1055–1064 (1999)

    Article  Google Scholar 

  2. Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 3642–3649 (2012)

    Google Scholar 

  3. Deng, L., Abdel-Hamid, O., Yu, D.: A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 6669–6673 (2013)

    Google Scholar 

  4. Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)

  5. Krizhevsky, A.: Learning multiple layers of features from tiny images. Unpublished

    Google Scholar 

  6. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  7. LeCun, Y., Denker, J., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems, Citeseer (1990)

    Google Scholar 

  8. Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning, ACM, pp. 609–616 (2009)

    Google Scholar 

  9. Lin, M., Chen, Q., Yan, S.: Network in network. CoRR, abs/1312.4400, 2013

    Google Scholar 

  10. Pass, G., Zabih, R.: Histogram refinement for content-based image retrieval. In: Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision, WACV’96. IEEE, pp. 96–102 (1996)

    Google Scholar 

  11. Sermanet, P., Chintala, S., LeCun, Y.: Convolutional neural networks applied to house numbers digit classification. In: 21st International Conference on Pattern Recognition (ICPR). IEEE, pp. 3288–3291 (2012)

    Google Scholar 

  12. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229 (2013)

  13. Swain, M.J., Ballard, D.H.: Indexing via color histograms. In: Active Perception and Robot Vision. Springer, pp. 261–273. (1992)

    Google Scholar 

  14. Wan, L., Zeiler, M., Zhang, S., LeCun, Y., Fergus, R.: Regularization of neural networks using dropconnect. In: Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp. 1058–1066 (2013)

    Google Scholar 

  15. Xiao, J., Hays, J., Ehinger, K.A.: Aude Oliva, and Antonio Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In: 2010 IEEE conference on Computer vision and pattern recognition (CVPR). IEEE, pp. 3485–3492. (2010)

    Google Scholar 

  16. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional neural networks. arXiv preprint arXiv:1311.2901 (2013)

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Correspondence to Zejia Zheng .

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Zheng, Z., Li, Z., Nagar, A. (2015). Compact Deep Neural Networks for Device-Based Image Classification. In: Hua, G., Hua, XS. (eds) Mobile Cloud Visual Media Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-24702-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-24702-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24700-7

  • Online ISBN: 978-3-319-24702-1

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