The Effect of Color Channel Representations on the Transferability of Convolutional Neural Networks

  • Javier Diaz-CelyEmail author
  • Carlos Arce-Lopera
  • Juan Cardona Mena
  • Lina Quintero
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 943)


Image classification is one of the most important tasks in computer vision, since it can be used to retrieve, store, organize, and analyze digital images. In recent years, deep learning convolutional neural networks have been successfully used to classify images surpassing previous state of the art performances. Moreover, using transfer learning techniques, very complex models have been successfully utilized for other tasks different from the original task for which they were trained for. Here, the influence of the color representation of the input images was tested when using a transfer learning technique in three different well-known convolutional models. The experimental results showed that color representation in the CIE-L*a*b* color space gave reasonably good results compared to the RGB color format originally used during training. These results support the idea that the features learned can be transferred to new models with images using different color channels such as the CIE-L*a*b* space, and opens up new research questions as to the transferability of image representation in convolutional neural networks.


Color channel representation Convolutional deep learning Transfer learning 



This work was funded by Universidad Icesi through its institutional research support program.


  1. 1.
    Bengio, Y., Bastien, F., Bergeron, A., Boulanger-Lewandowski, N., Breuel, T.M., Chherawala, Y., Cissé, M., Côté, M., Erhan, D., Eustache, J., Glorot, X., Muller, X., Lebeuf, S.P., Pascanu, R., Rifai, S., Savard, F., Sicard, G.: Deep learners benefit more from out-of-distribution examples. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2011, Fort Lauderdale, USA, 11–13 April 2011, pp. 164–172 (2011)Google Scholar
  2. 2.
    Csaáji, B.C.: Approximation with artificial neural networks. Ph.D. dissertation, Faculty of Sciences, Eötvös Loránd University, Hungary (2001)Google Scholar
  3. 3.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR (2009)Google Scholar
  4. 4.
    Garcia-Gasulla, D., Vilalta, A., Parés, F., Moreno, J., Ayguadé, E., Labarta, J., Cortés, U., Suzumura, T.: An out-of-the-box full-network embedding for convolutional neural networks. CoRR abs/1705.07706 (2017)Google Scholar
  5. 5.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)Google Scholar
  6. 6.
    Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: efficient convolutional neural networks for mobile vision applications. CoRR abs/1704.04861 (2017)Google Scholar
  7. 7.
    Kaur, A., Kranthi, B.: Comparison between YCbCr color space and CIELab color space for skin color segmentation. Int. J. Appl. Inf. Syst. 3(4), 30–33 (2012). Published by Foundation of Computer Science, New York, USAGoogle Scholar
  8. 8.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates, Inc. (2012)Google Scholar
  9. 9.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  10. 10.
    Podpora, M., Korbaś, G.P., Kawala-Janik, A.: YUV vs RGB–choosing a color space for human-machine interaction. In: Ganzha, M.P.M., Maciaszek, L. (eds.) Position Papers of the 2014 Federated Conference on Computer Science and Information Systems. Annals of Computer Science and Information Systems, vol. 3, pp. 29–34. PTI (2014).
  11. 11.
    Razavian, A.S., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014, pp. 512–519. IEEE Computer Society, Washington (2014)Google Scholar
  12. 12.
    Shin, M.C., Chang, K.I., Tsap, L.V.: Does colorspace transformation make any difference on skin detection? In: Sixth IEEE Workshop on Applications of Computer Vision (WACV 2002), pp. 275-279 (2002)Google Scholar
  13. 13.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)Google Scholar
  14. 14.
    Srivastava, M.M., Kant, S.: Visual aesthetic analysis using deep neural network: model and techniques to increase accuracy without transfer learning. CoRR abs/1712.03382 (2017)Google Scholar
  15. 15.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9 (2015)Google Scholar
  16. 16.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016)Google Scholar
  17. 17.
    Xu, X., Li, Y., Wu, G., Luo, J.: Multi-modal deep feature learning for RGB-D object detection. Pattern Recogn. 72(C), 300–313 (2017)CrossRefGoogle Scholar
  18. 18.
    Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Proceedings of the 27th International Conference on Neural Information Processing Systems, NIPS 2014, vol. 2, pp. 3320–3328. MIT Press (2014)Google Scholar
  19. 19.
    Zeng, Y., Xu, X., Shen, D., Fang, Y., Xiao, Z.: Traffic sign recognition using kernel extreme learning machines with deep perceptual features. IEEE Trans. Intell. Transp. Syst. 18(6), 1647–1653 (2017)Google Scholar
  20. 20.
    Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: 14th European Conference on Computer Vision - ECCV 2016, Amsterdam, The Netherlands, pp. 649–666 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Javier Diaz-Cely
    • 1
    Email author
  • Carlos Arce-Lopera
    • 1
  • Juan Cardona Mena
    • 1
  • Lina Quintero
    • 1
  1. 1.Universidad IcesiCaliColombia

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