Skip to main content
SpringerLink
Log in

Beyond Eleven Color Names for Image Understanding

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Color description is one of the fundamental problems of image understanding. One of the popular ways to represent colors is by means of color names. Most existing work on color names focuses on only the eleven basic color terms of the English language. This could be limiting the discriminative power of these representations, and representations based on more color names are expected to perform better. However, there exists no clear strategy to choose additional color names. We collect a dataset of 28 additional color names. To ensure that the resulting color representation has high discriminative power we propose a method to order the additional color names according to their complementary nature with the basic color names. This allows us to compute color name representations with high discriminative power of arbitrary length. In the experiments we show that these new color name descriptors outperform the existing color name descriptor on the task of visual tracking, person re-identification and image classification.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. Because the Lab space is perceptually uniform, we discretize it into equal volume bins. Different quantization levels per channel are chosen because of the different ranges: The intensity axis ranges from 0 to 100, and the chromatic axes range from \(-\,100\) to 100.

  2. We also experimented with selecting the color name with the lowest mean correlation but results were inferior.

  3. We found that more complex kernels such as intersection did not improve results.

  4. We found for optimal results were obtained with a \(\lambda =1\) for 11 color names, a \(\lambda =0.9\) for 15 color names and a \(\lambda =0.8\) for 25 color names.

References

  1. Benavente, R., Vanrell, M., Bladrich, R.: A data set for fuzzy colour naming. COLOR Res. Appl. 31(1), 48–56 (2006)

    Article  Google Scholar 

  2. Benavente, R., Vanrell, M., Baldrich, R.: Parametric fuzzy sets for automatic color naming. J. Opt. Soc. Am. A 25(10), 2582–2593 (2008)

    Article  Google Scholar 

  3. Benavente, R., Van de Weijer, J., Vanrell, M., Schmid, C., Baldrich, R., Verbeek, J., Larlus, D.: Color names. In: Gevers, T., Gijsenij, A., van de Weijer, J., Geusebroek, J.M. Color in Computer Vision. Wiley, New York (2012)

  4. Berlin, B., Kay, P.: Basic Color Terms: Their Universality and Evolution. University of California, Berkeley (1969)

    Google Scholar 

  5. Boynton, R.M., Olson, C.X.: Locating basic colors in the OSA space. Color Res. Appl. 12(2), 94–105 (1987)

    Article  Google Scholar 

  6. Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, Prague, vol. 1, pp. 1–2 (2004)

  7. Danelljan, M., Khan, F.S., Felsberg, M., van de Weijer, J.: Adaptive color attributes for real-time visual tracking. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1090–1097. IEEE (2014)

  8. Danelljan, M., Häger, G., Khan, F.S., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: ICCV (2015)

  9. Finlayson G.D., Schiele, B., Crowley, J.L.: Comprehensive colour image normalization. In ECCV ’98: Proceedings of the 5th European Conference on Computer Vision, vol. I, pp. 475–490. Springer (1998). ISBN 3-540-64569-1

  10. Geusebroek, J.M., van den Boomgaard, R., Smeulders, A.W.M., Geerts, H.: Color invariance. PAMI 23(12), 1338–1350 (2001)

    Article  Google Scholar 

  11. Gevers, T., Smeulders, A.: Color based object recognition. Pattern Recognit. 32, 453–464 (1999)

    Article  Google Scholar 

  12. Hardin, C.L., Maffi, L. (eds.): Color Categories in Thought and Language. Cambridge University Press, Cambridge (1997)

    Google Scholar 

  13. Healey, G.: Segmenting images using normalized color. IEEE Trans. Syst. Man Cybern. 22, 64–73 (1992)

    Article  Google Scholar 

  14. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: European Conference on Computer Vision, pp. 702–715. Springer (2012)

  15. Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57. ACM (1999)

  16. Hong, Z., Chen, Z., Wang, C., Mei, X., Prokhorov, D., Tao, D.: Multi-store tracker (muster): a cognitive psychology inspired approach to object tracking. In: CVPR (2015)

  17. Huynh, C.P., Robles-Kelly, A.: A solution of the dichromatic model for multispectral photometric invariance. Int. J. Comput. Vis 90(1), 1–27 (2010)

    Article  Google Scholar 

  18. Kelly, K.L., Judd, D.B.: Color: Universal Language and Dictionary of Names, vol. 440. US Department of Commerce, National Bureau of Standards, Gaithersburg (1976)

    Book  Google Scholar 

  19. Khan, F.S., Weijer, J., Bagdanov, A.D., Vanrell, M.: Portmanteau vocabularies for multi-cue image representation. In: Advances in Neural Information Processing Systems, pp. 1323–1331 (2011)

  20. Khan, F.S., Anwer, R.M., van de Weijer, J., Bagdanov, A.D., Vanrell, M., Lopez, A.M.: Color attributes for object detection. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3306–3313. IEEE (2012)

  21. Khan, F.S., Van de Weijer, J., Vanrell, M.: Modulating shape features by color attention for object recognition. Int. J. Comput. Vis. 98(1), 49–64 (2012)

    Article  Google Scholar 

  22. Khan, F.S., Anwer, R.M., van de Weijer, J., Bagdanov, A.D., Lopez, A.M., Felsberg, M.: Coloring action recognition in still images. Int. J. Comput. Vis. 105(3), 205–221 (2013a)

    Article  Google Scholar 

  23. Khan, R., Van de Weijer, J., Khan, F.S., Muselet, D., Ducottet, C., Barat, C.: Discriminative color descriptors. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2866–2873. IEEE (2013)

  24. 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)

  25. LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)

    Article  Google Scholar 

  26. Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. In: ECCV Workshop (2014)

  27. Liu, X., Wang, H., Wu, Y., Yang, J., Yang, H.: An ensemble color model for human re-identification. In: 2015 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 868–875. IEEE (2015)

  28. Liu, Y., Zhang, D., Lu, G., Ma, W-Y.: Region-based image retrieval with high-level semantic color names. In: Proceedings of the 11th International Multimedia Modelling Conference, 2005 (MMM 2005), pp. 180–187. IEEE (2005)

  29. Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3730–3738 (2015)

  30. Mojsilovic, A.: A computational model for color naming and describing color composition of images. IEEE Trans. Image Process. 14(5), 690–699 (2005)

    Article  Google Scholar 

  31. Mylonas, D., Griffin, D., Purver, L.D., Katemake, P., Davidoff J.: The role of primary colours in colour naming. (2016) (Under review)

  32. Mylonas, D., MacDonald, L.: Augmenting basic colour terms in english. Color Res. Appl. 41, 32–42 (2015)

    Article  Google Scholar 

  33. Nilsback, M.-E., Zisserman, A.: Automated flower classification over a large number of classes. In: Sixth Indian Conference on Computer Vision, Graphics & Image Processing, 2008 (ICVGIP’08), pp. 722–729. IEEE (2008)

  34. Párraga, C., Benavente, R., Baldrich, R., Vanrell, M.: Psychophysical measurements to model intercolor regions of color-naming space. J. Imaging Sci. Technol. 53(3), 31106-1 (2009)

    Article  Google Scholar 

  35. Parraga, C.A., Akbarinia, A.: NICE: A computational solution to close the gap from colour perception to colour categorization. PLoS ONE 11(3), e0149538 (2016)

    Article  Google Scholar 

  36. Schwartz, G., Nishino, K.: Discovering perceptual attributes in a deep local material recognition network. arXiv preprint arXiv:1604.01345 (2016)

  37. Sharif Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 806–813 (2014)

  38. Sturges, J., Whitfield, T.W.: Locating basic colours in the munsell space. Color Res. Appl. 20(6), 364–376 (1995)

    Article  Google Scholar 

  39. van de Sande, K.E.A., Theo, G., Cees, G., Snoek, M.: Evaluating color descriptors for object and scene recognition. PAMI 32(9), 1582–1596 (2010)

    Article  Google Scholar 

  40. Van De Weijer, J., Khan, F.S.: An overview of color name applications in computer vision. In: International Workshop on Computational Color Imaging, pp. 16–22. Springer (2015)

  41. van de Weijer, J., Schmid, C.: Applying color names to image description. In: IEEE International Conference on Image Processing (ICIP), San Antonio, USA (2007)

  42. 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)

    Article  MathSciNet  MATH  Google Scholar 

  43. van de Weijer, J., Schmid, C., Verbeek, J., Larlus, D.: Learning color names for real-world applications. IEEE Trans. Image Process. 18(7), 1512–1524 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  44. Wang, Y., Li, S., Kot, A.C.: On branded handbag recognition. IEEE Trans. Multimedia 18(9), 1869–1881 (2016)

    Article  Google Scholar 

  45. Wang, Y., Liu, J., Wang, J., Li, Y., Lu, H.: Color names learning using convolutional neural networks. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 217–221. IEEE (2015)

  46. Wu, Y, Lim, J., Yang, M-H.: Online object tracking: a benchmark. In: 2013 IEEE Conference on Computer Vision and Pattern Recogniton (CVPR). IEEE (2013)

  47. Yang, Y., Yang, J., Yan, J., Liao, S., Yi, D., Li, S.: Salient color names for person re-identification. In: 2014 European Conference on Computer Vision (ECCV), pp. 536–551. Springer (2014)

  48. Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: IEEE International Conference on Computer Vision (ICCV) (2015)

  49. Zollinger, H.: Why just turquoise? Remarks on the evolution of color terms. Psychol. Res. 46(4), 403–409 (1984)

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge Dimitris Mylonas for his helpful suggestion on extending the color name set. We also acknowledge Nicole Walasek who has been of great help in the dataset collection and PLSA code preparation. Lu Yu acknowledges the Chinese Scholarship Council (CSC) grant No.201506290126. This work was supported by TIN2013-41751-P and TIN2016-79717-R of the Spanish Ministry and the CERCA Programme / Generalitat de Catalunya.

Author information

Authors and Affiliations

  1. Key Laboratory of Information Fusion Technology, Northwestern Polytechnical University, Xi’an, 710072, China

    Lu Yu & Yongmei Cheng

  2. Computer Vision Laboratory, Linköping University, 581 83, Linköping, Sweden

    Fahad Shahbaz Khan

  3. Computer Vision Center/Computer Science Department, Universitat Autonoma de Barcelona, 08193, Barcelona, Spain

    Lu Yu, Lichao Zhang, Joost van de Weijer & C. Alejandro Parraga

Authors
  1. Lu Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lichao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joost van de Weijer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fahad Shahbaz Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yongmei Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. C. Alejandro Parraga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lu Yu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, L., Zhang, L., van de Weijer, J. et al. Beyond Eleven Color Names for Image Understanding. Machine Vision and Applications 29, 361–373 (2018). https://doi.org/10.1007/s00138-017-0902-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-017-0902-y

Keywords

Search

Navigation