Learning Color Names from Real-World Images

Within a computer vision context color naming is the action of assigning linguistic color labels to image pixels. In general, research on color naming applies the following paradigm: a collection of color chips is labelled with color names within a well-defined experimental setup by multiple test subjects. The collected data set is subsequently used to label RGB values in real-world images with a color name. Apart from the fact that this collection process is time consuming, it is unclear to what extent color naming within a controlled setup is representative for color naming in real-world images. Therefore we propose to learn color names from real-world images. Furthermore, we avoid test subjects by using Google Image to collect a data set. Due to limitations of Google Image this data set contains a sub- stantial quantity of wrongly labelled data. The color names are learned using a PLSA model adapted to this task. Experimental results show that color names learned from real- world images significantly outperform color names learned from labelled color chips on retrieval and classification.

@InProceedings{VSV07,
  author       = "Joost van de Weijer and Cordelia Schmid and Jakob Verbeek",
  title        = "Learning Color Names from Real-World Images",
  booktitle    = "Conference on Computer Vision \& Pattern Recognition",
  pages        = "1--8",
  month        = "jun",
  year         = "2007",
  keywords     = "LEAR, LJK, CLASS",
  url          = "http://lear.inrialpes.fr/pubs/2007/VSV07"
}