Learning Color Names from Real-World Images

Joost Van de Weijer 1 Cordelia Schmid 1 Jakob Verbeek 1
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
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 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.
Type de document :
Communication dans un congrès
CVPR 2007 - IEEE Conference on Computer Vision & Pattern Recognition, Jun 2007, Minneapolis, United States. IEEE Computer society, pp.1-8, 2007, 〈http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4270243〉. 〈10.1109/CVPR.2007.383218〉
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https://hal.inria.fr/inria-00321127
Contributeur : Jakob Verbeek <>
Soumis le : lundi 11 avril 2011 - 11:22:34
Dernière modification le : mercredi 11 avril 2018 - 01:58:54
Document(s) archivé(s) le : mardi 12 juillet 2011 - 02:41:17

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Joost Van de Weijer, Cordelia Schmid, Jakob Verbeek. Learning Color Names from Real-World Images. CVPR 2007 - IEEE Conference on Computer Vision & Pattern Recognition, Jun 2007, Minneapolis, United States. IEEE Computer society, pp.1-8, 2007, 〈http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4270243〉. 〈10.1109/CVPR.2007.383218〉. 〈inria-00321127v2〉

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