Beyond eleven color names for image understanding
Yu, Lu (Centre de Visió per Computador (Bellaterra, Catalunya))
Zhang, Lichao (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
Weijer, Joost van de (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
Khan, Fahad Shahbaz (Linköping University. Computer Vision Laboratory)
Cheng, Yongmei (Northwestern Polytechnical University. Key Laboratory of Information Fusion Technology (China))
Parraga, Carlos Alejandro (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)

Date:	2018
Abstract:	Color description is one of the fundamental problems of image understanding. One of the popular ways to represent colors is by means of color names. Most existing work on color names focuses on only the eleven basic color terms of the English language. This could be limiting the discriminative power of these representations, and representations based on more color names are expected to perform better. However, there exists no clear strategy to choose additional color names. We collect a dataset of 28 additional color names. To ensure that the resulting color representation has high discriminative power we propose a method to order the additional color names according to their complementary nature with the basic color names. This allows us to compute color name representations with high discriminative power of arbitrary length. In the experiments we show that these new color name descriptors outperform the existing color name descriptor on the taskof visual tracking, person re-identification and image classification.
Grants:	Ministerio de Economía y Competitividad TIN2013-41751-P Agencia Estatal de Investigación TIN2016-79717-R
Note:	Altres ajuts: CERCA Programme/Generalitat de Catalunya
Rights:	Tots els drets reservats.
Language:	Anglès
Document:	Article ; recerca ; Versió acceptada per publicar
Subject:	Color name ; Discriminative descriptors ; Image classification ; Re-identification ; Tracking
Published in:	Machine Vision and Applications, Vol. 29, Issue 2 (February 2018) , p. 361-373, ISSN 1432-1769

DOI: 10.1007/s00138-017-0902-y

Postprint 14 p, 3.9 MB

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