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Learning Color Names for Real-World Applications

Joost van de Weijer 1 Cordelia Schmid 1 Jakob Verbeek 1 Diane Larlus 1
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : 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.
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Joost van de Weijer, Cordelia Schmid, Jakob Verbeek, Diane Larlus. Learning Color Names for Real-World Applications. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2009, 18 (7), pp.1512-1523. ⟨10.1109/TIP.2009.2019809⟩. ⟨inria-00439284v2⟩



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