Towards automatic concept transfer

This paper introduces a novel approach to automatic concept transfer; examples of concepts are "romantic", "earthy", and "luscious". The approach modifies the color content of an input image given only a concept specified by a user in natural language, thereby requiring minimal user input. This approach is particularly useful for users who are aware of the message they wish to convey in the transferred image while being unsure of the color combination needed to achieve the corresponding transfer. The user may adjust the intensity level of the concept transfer to his/her liking with a single parameter. The proposed approach uses a convex clustering algorithm, with a novel pruning mechanism, to automatically set the complexity of models of chromatic content. It also uses the Earth-Mover's Distance to compute a mapping between the models of the input image and the target chromatic concept. Results show that our approach yields transferred images which effectively represent concepts, as confirmed by a user study.

[1]  Erik Reinhard,et al.  Progressive histogram reshaping for creative color transfer and tone reproduction , 2010, NPAR.

[2]  Polina Golland,et al.  Convex Clustering with Exemplar-Based Models , 2007, NIPS.

[3]  L. Ou,et al.  A study of colour emotion and colour preference. Part II: Colour emotions for two‐colour combinations , 2004 .

[4]  Lizhuang Ma,et al.  Color transfer in correlated color space , 2006, VRCIA '06.

[5]  Leatrice Eiseman.  Pantone Guide to Communicating with Color , 2000 .

[6]  Bernhard Schölkopf,et al.  Automatic Image Colorization Via Multimodal Predictions , 2008, ECCV.

[7]  Hwann-Tzong Chen,et al.  Landmark-based sparse color representations for color transfer , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[8]  Dani Lischinski,et al.  Colorization using optimization , 2004, ACM Trans. Graph..

[9]  Garrett M. Johnson,et al.  Color Imaging: Fundamentals and Applications , 2008 .

[10]  L. Ou,et al.  A study of colour emotion and colour preference. Part I: Colour emotions for single colours , 2004 .

[11]  Daniel Cohen-Or,et al.  Image Appearance Exploration by Model‐Based Navigation , 2009, Comput. Graph. Forum.

[12]  Fabio Pellacini,et al.  User‐Controllable Color Transfer , 2010, Comput. Graph. Forum.

[13]  L. Ou,et al.  A study of colour emotion and colour preference. Part III: Colour preference modeling , 2004 .

[14]  Gabriela Csurka,et al.  Learning moods and emotions from color combinations , 2010, ICVGIP '10.

[15]  Tien-Tsin Wong,et al.  Manga colorization , 2006, ACM Trans. Graph..

[16]  Guanbo Bao,et al.  Fast local color transfer via dominant colors mapping , 2010, SIGGRAPH ASIA.

[17]  Erik Reinhard,et al.  Color Transfer between Images , 2001, IEEE Computer Graphics and Applications.

[18]  Daniel Freedman,et al.  Object-to-object color transfer: Optimal flows and SMSP transformations , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  Chun Chen,et al.  Data-driven image color theme enhancement , 2010, SIGGRAPH 2010.

[20]  Tien-Tsin Wong,et al.  Manga colorization , 2006, SIGGRAPH 2006.

[21]  Liqing Zhang,et al.  Color conceptualization , 2007, ACM Multimedia.

[22]  Anil Kokaram,et al.  The linear Monge-Kantorovitch linear colour mapping for example-based colour transfer , 2007 .

[23]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[24]  Alexei A. Efros,et al.  Using Color Compatibility for Assessing Image Realism , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[25]  Chi-Keung Tang,et al.  Local color transfer via probabilistic segmentation by expectation-maximization , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[26]  Donald H. House,et al.  Image Recoloring Induced by Palette Color Associations , 2003, WSCG.

[27]  A.C. Kokaram,et al.  N-dimensional probability density function transfer and its application to color transfer , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[28]  Chuan-Kai Yang,et al.  Automatic Mood-Transferring between Color Images , 2008, IEEE Computer Graphics and Applications.