PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning Moods and Emotions from Color Combinations
Luca Marchesotti, Gabriela Csurka, Craig Saunders and S Skaff
In: ICVGIP (Indian Conference on Computer Vision, Graphics and Image Processing), Chennai, India(2010).


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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:7606
Deposited By:Craig Saunders
Deposited On:17 March 2011