Beyond Eleven Color Names for Image Understanding

Lu Yu, Lichao Zhang, Joost van de Weijer, Fahad Shahbaz Khan, Yongmei Cheng, C. Alejandro Parraga

Research output: Contribution to journalArticleResearchpeer-review

13 Citations (Scopus)


© 2017, Springer-Verlag GmbH Germany, part of Springer Nature. 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.
Original languageEnglish
Pages (from-to)361-373
JournalMachine Vision and Applications
Issue number2
Publication statusPublished - 1 Feb 2018


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


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