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 realworld 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 substantial 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 realworld images...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Joost van de Weijer, Cordelia Schmid, Jakob J. Verbeek
• Keywords: Color Chips, Color Names, Color Naming, Computer Vision, Context Color Naming, CVPR 2007, Linguistic Color Labels