PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning Color Names from Real-World Images
Joost van de Weijer, Cordelia Schmid and Jakob Verbeek
In: CVPR(2007).

Abstract

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 labeled 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 substantial quantity of wrongly labeled 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 labeled color chips on retrieval and classification.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:4217
Deposited By:Jakob Verbeek
Deposited On:03 December 2008