Using Association Rules to Discover Color-Emotion Relationships Based on Social Tagging

Relationships between colors and emotions have been studied for a long time in several domains, such as psychology and artistic theories. In this paper, we extract such relations appearing in social tagging systems, in which users can freely choose the images they upload and annotate, as well as the annotation tags. We first study two color representations that can be used to encode the chromatic contents of such images and select the most appropriate one for discovering coloremotion relationships, based on their performance for a classification task. We then extract, from this image corpus and based on the selected encoding, association rules characterizing relations between colors and emotions. We use the Apriori algorithm with a particular focus on the implications of color presence and absence on the emotion presences, commenting and discussing the obtained results.

[1]  J. Itten.  The art of color : the subjective experience and objective rationale of color , 1973 .

[2]  Yuejin Zhang,et al.  A Survey of Interestingness Measures for Association Rules , 2009, 2009 International Conference on Business Intelligence and Financial Engineering.

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

[4]  C. W. Hughes.  Emotion: Theory, Research and Experience , 1982 .

[5]  P. Lang.  International Affective Picture System (IAPS) : Technical Manual and Affective Ratings , 1995 .

[6]  Changle Zhou,et al.  Content-Based Affective Image Classification and Retrieval Using Support Vector Machines , 2005, ACII.

[7]  Mingyu Lu,et al.  The Optimization and Improvement of the Apriori Algorithm , 2008, 2008 International Symposium on Intelligent Information Technology Application Workshops.

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

[9]  Yu Ying-lin,et al.  Image Retrieval by Emotional Semantics: A Study of Emotional Space and Feature Extraction , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[10]  Alan Costall,et al.  The emotional connotations of color: A qualitative investigation , 2008 .

[11]  Jan M. Zytkow,et al.  Handbook of Data Mining and Knowledge Discovery , 2002 .

[12]  Masafumi Hagiwara,et al.  Image query by impression words-the IQI system , 1998 .

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

[14]  M. Hemphill,et al.  A note on adults' color-emotion associations. , 1996, The Journal of genetic psychology.

[15]  P. Lang,et al.  International Affective Picture System (IAPS): Instruction Manual and Affective Ratings (Tech. Rep. No. A-4) , 1999 .

[16]  Shengming Jiang,et al.  Image Retrieval by Emotional Semantics: A Study of Emotional Space and Feature Extraction , 2006, SMC.

[17]  Lei Zhang,et al.  A CBIR method based on color-spatial feature , 1999, Proceedings of IEEE. IEEE Region 10 Conference. TENCON 99. 'Multimedia Technology for Asia-Pacific Information Infrastructure' (Cat. No.99CH37030).

[18]  Patrick Meyer,et al.  Association Rule Interestingness Measures: Experimental and Theoretical Studies , 2007, Quality Measures in Data Mining.

[19]  Fabrice Guillet,et al.  Quality Measures in Data Mining , 2009, Studies in Computational Intelligence.

[20]  Wolfgang G. Stock,et al.  Collective indexing of emotions in images. A study in emotional information retrieval , 2009 .

[21]  Tao Zhang,et al.  Image Emotional Classification Based on Color Semantic Description , 2008, ADMA.

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