Beyond Eleven Color Names for Image Understanding

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.

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Notes

  1. 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. 2.

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

  3. 3.

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

  4. 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.

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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.

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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

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Keywords

  • Color name
  • Discriminative descriptors
  • Image classification
  • Re-identification
  • Tracking