A computational model of color perception and color naming
A computational model of color perception and color naming
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
IEEE Computer Graphics and Applications
Local Color Transfer via Probabilistic Segmentation by Expectation-Maximization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
ACM SIGGRAPH 2006 Papers
Automatic Mood-Transferring between Color Images
IEEE Computer Graphics and Applications
Learning color names for real-world applications
IEEE Transactions on Image Processing
Adapted vocabularies for generic visual categorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Color compatibility from large datasets
ACM SIGGRAPH 2011 papers
Towards automatic concept transfer
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Non-Photorealistic Animation and Rendering
Special Section on CANS: Toward automatic and flexible concept transfer
Computers and Graphics
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In this paper, we tackle the problem of associating combinations of colors to abstract categories (e.g. capricious, classic, cool, delicate, etc.). It is evident that such concepts would be difficult to distinguish using single colors, therefore we consider combinations of colors or color palettes. We leverage two novel databases for color palettes and we learn categorization models using low and high level descriptors. Preliminary results show that Fisher representation based on GMMs is the most rewarding strategy in terms of classification performance over a baseline model. We also suggest a process for cleaning weakly annotated data, whilst preserving the visual coherence of categories. Finally, we demonstrate how learning abstract categories on color palettes can be used in the application of color transfer, personalization and image re-ranking.