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Color Naming for Multi-color Fashion Items

  • Vacit Oguz Yazici
  • Joost van de Weijer
  • Arnau Ramisa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 747)

Abstract

There exists a significant amount of research on color naming of single colored objects. However in reality many fashion objects consist of multiple colors. Currently, searching in fashion datasets for multi-colored objects can be a laborious task. Therefore, in this paper we focus on color naming for images with multi-color fashion items. We collect a dataset, which consists of images which may have from one up to four colors. We annotate the images with the 11 basic colors of the English language. We experiment with several designs for deep neural networks with different losses. We show that explicitly estimating the number of colors in the fashion item leads to improved results.

Keywords

Deep learning Color Multi-label 

Notes

Acknowledgements

This work was supported by TIN2016-79717-R of the Spanish Ministry and the CERCA Programme and the Industrial Doctorate Grant 2016 DI 039 of the Ministry of Economy and Knowledge of the Generalitat de Catalunya.

References

  1. 1.
    Yamaguchi, K., Kiapour, M.H., Ortiz, L.E., Berg, T.L.: Parsing clothing in fashion photographs. In: CVPR, pp. 3570–3577. IEEE (2012)Google Scholar
  2. 2.
    Simo-Serra, E., Fidler, S., Moreno-Noguer, F., Urtasun, R.: A high performance CRF model for clothes parsing. In: ACCV (2014)Google Scholar
  3. 3.
    Cervantes, E., Yu, L.L., Bagdanov, A.D., Masana, M., van de Weijer, J.: Hierarchical part detection with deep neural networks. In: ICIP, pp. 1933–1937 (2016)Google Scholar
  4. 4.
    van de Weijer, J., Schmid, C., Verbeek, J., Larlus, D.: Learning color names for real-world applications. IEEE Trans. Image Process. 18(7), 1512–1523 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Benavente, R., Vanrell, M., Baldrich, R.: Parametric fuzzy sets for automatic color naming. JOSA A 25(10), 2582–2593 (2008)CrossRefGoogle Scholar
  6. 6.
    Berlin, B., Kay, P.: Basic color terms: their universality and evolution. University of California, Berkeley (1969)Google Scholar
  7. 7.
    van de Weijer, J., Khan, F.S.: An overview of color name applications in computer vision. In: CCIW, pp. 16–22 (2015)Google Scholar
  8. 8.
    Liu, S., Feng, J., Domokos, C., Xu, H., Huang, J., Hu, Z., Yan, S.: Fashion parsing with weak color-category labels. IEEE Trans. Multimedia 16(1), 253–265 (2014)CrossRefGoogle Scholar
  9. 9.
    Liu, Z., Luo, P., Qiu, S., Wang, X., Tang, X.: DeepFashion: powering robust clothes recognition and retrieval with rich annotations. In: CVPR (2016)Google Scholar
  10. 10.
    Cheng, Z., Li, X., Loy, C.C.: Pedestrian color naming via convolutional neural network. In: ACCV, pp. 35–51. Springer (2016)Google Scholar
  11. 11.
    Wang, Y., Liu, J., Wang, J., Li, Y., Lu, H.: Color names learning using convolutional neural networks. In: ICIP, pp. 217–221. IEEE (2015)Google Scholar
  12. 12.
    Mylonas, D., MacDonald, L., Wuerger, S.: Towards an online color naming model. In: Color and Imaging Conference, vol. 2010, pp. 140–144. Society for Imaging Science and Technology (2010)Google Scholar
  13. 13.
    Schauerte, B., Fink, G.A.: Web-based learning of naturalized color models for human-machine interaction. In: DICTA, pp. 498–503. IEEE (2010)Google Scholar
  14. 14.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)Google Scholar
  15. 15.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248–255. IEEE (2009)Google Scholar
  16. 16.
    Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: ICCV, pp. 3730–3738 (2015)Google Scholar
  17. 17.
    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 International Conference on Multimedia, pp. 675–678. ACM (2014)Google Scholar
  18. 18.
    Mylonas, D., MacDonald, L.: Augmenting basic colour terms in English. Color Res. Appl. 41(1), 32–42 (2015)CrossRefGoogle Scholar
  19. 19.
    Yu, L., Zhang, L., van de Weijer, J., Khan, F.S., Cheng, Y., Parraga, C.A.: Beyond eleven color names for image understanding. Mach. Vis. Appl. 29(2), 361–373 (2018)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Vacit Oguz Yazici
    • 1
    • 2
  • Joost van de Weijer
    • 1
  • Arnau Ramisa
    • 2
  1. 1.Computer Vision CenterUniversitat Autonoma de BarcelonaBellaterra, BarcelonaSpain
  2. 2.Wide Eyes TechnologiesBarcelonaSpain

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