Applying Color Names to Image Description

Photometric invariance is a desired property for color image descriptors. It ensures that the description has a certain robustness with respect to scene incidental variations such as changes in viewpoint, object orientation, and illuminant color. A drawback of photometric invariance is that the discriminative power of the description reduces while increasing the photometric invariance. In this paper, we look into the use of color names for the purpose of image description. Color names are linguistic labels that humans attach to colors. They display a certain amount of photometric invariance, and as an additional advantage allow the description of the achromatic colors, which are undistinguishable in a photometric invariant representation. Experiments on an image classification task show that color description based on color names outperforms description based on photometric invariants.

[1]  Jan-Mark Geusebroek,et al.  Compact Object Descriptors from Local Colour Invariant Histograms , 2006, BMVC.

[2]  P. Kay Basic Color Terms: Their Universality and Evolution , 1969 .

[3]  Ying Liu,et al.  Region-Based Image Retrieval with High-Level Semantic Color Names , 2005, 11th International Multimedia Modelling Conference.

[4]  C. L. Hardin,et al.  Color categories in thought and language: Author index , 1997 .

[5]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[6]  Cordelia Schmid,et al.  Coloring Local Feature Extraction , 2006, ECCV.

[7]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[8]  H. S. Straight Color Categories in Thought and Language , 2003 .

[9]  Andrew Zisserman,et al.  A Visual Vocabulary for Flower Classification , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[10]  A. Mojsilovi A Computational Model for Color Naming and Describing Color Composition of Images , 2022 .

[11]  P. Kay,et al.  Basic Color Terms: Their Universality and Evolution , 1973 .

[12]  Lixin Fan,et al.  Categorizing Nine Visual Classes using Local Appearance Descriptors , 2004 .

[13]  Andrew Zisserman,et al.  Scene Classification Via pLSA , 2006, ECCV.

[14]  Cordelia Schmid,et al.  Learning Color Names from Real-World Images , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Robert Benavente,et al.  A data set for fuzzy colour naming , 2006 .