PT Journal
AU Joost Van de Weijer
   Cordelia Schmid
   Jakob Verbeek
   Diane Larlus
TI Learning Color Names for Real-World Applications
SO IEEE Transaction in Image Processing
JI TIP
PY 2009
BP 1512–1524
VL 18
IS 7
DI 10.1109/TIP.2009.2019809
AB Color names are required in real-world applications such as image retrieval and image annotation. Traditionally, they are learned from a collection of labelled color chips. These color chips are labelled with color names within a well-defined experimental setup by human test subjects. However naming colors in real-world images differs significantly from this experimental setting. In this paper, we investigate how color names learned from color chips compare to color names learned from real-world images. To avoid hand labelling real-world images with color names we use Google Image to collect a data set. Due to limitations of Google Image this data set contains a substantial quantity of wrongly labelled data. We propose several variants of the PLSA model to learn color names from this noisy data. Experimental results show that color names learned from real-world images significantly outperform color names learned from labelled color chips for both image retrieval and image annotation.
ER