Emotion Semantics Image Retrieval: An Brief Overview

Emotion is the most abstract semantic structure of images. This paper overviews recent research on emotion semantics image retrieval. First, the paper introduces the general frame of emotion semantics image retrieval and points out the four main research issues: to exact sensitive features from images, to define users’ emotion information, to build emotion user model and to individualize the user model. Then several algorithms to solve these four issues are analyzed in detail. After that, some future research topics, including construction of an emotion database, evaluation of the user model and computation of the user model, are discussed, and some resolved strategies are presented elementarily.

[1]  Kaori Yoshida,et al.  Database system for Kansei-oriented communication , 2000, 2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies.

[2]  Ingrid Zukerman,et al.  # 2001 Kluwer Academic Publishers. Printed in the Netherlands. Predictive Statistical Models for User Modeling , 1999 .

[3]  Lotfi A. Zadeh,et al.  Applied Soft Computing - Foreword , 2001, Appl. Soft Comput..

[4]  Toshikazu Kato,et al.  Image retrieval system using impression words , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[5]  Seiji Inokuchi,et al.  An attractiveness evaluation model based on the physical features of image regions , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[6]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  C. Osgood,et al.  The Measurement of Meaning , 1958 .

[8]  Sung-Bae Cho,et al.  Emotional image and musical information retrieval with interactive genetic algorithm , 2004, Proc. IEEE.

[9]  Hideyuki Kobayashi,et al.  The semantic network of KANSEI words , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[10]  R. Arnheim.  Art and Visual Perception, a Psychology of the Creative Eye , 1967 .

[11]  Toshikazu Kato,et al.  Multi-contrast based texture model for understanding human subjectivity , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

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

[13]  Toshikazu Kato,et al.  "Kansei" image retrieval system for street landscape-discrimination and graphical parameters based on correlation of two images , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[14]  David N. Chin.  Empirical Evaluation of User Models and User-Adapted Systems , 2001, User Modeling and User-Adapted Interaction.

[15]  K. Ozaki,et al.  Semantic retrieval on art museum database system , 1996, 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929).

[16]  T. Hayashi,et al.  An image retrieval system to estimate impression words from images using a neural network , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[17]  Toshikazu Kato,et al.  K-DIME: An Adaptive System to Retrieve Images from the WEB Using Subjective Criteria , 2000, DNIS.

[18]  William I. Grosky,et al.  Idea Grou p Inc . Copy right Idea Grou p Inc . Copy right Idea Grou p Inc . Copy right Idea Grou p Inc . Chapter II Bridging the Semantic Gap in Image Retrieval , 2018 .

[19]  Tetsuya Sato,et al.  Numerical expression of color emotion and its application , 2002, Other Conferences.

[20]  Alberto Del Bimbo,et al.  Retrieval of paintings using effects induced by color features , 1998, Proceedings 1998 IEEE International Workshop on Content-Based Access of Image and Video Database.

[21]  Bin Chen,et al.  Affective property of image and fractal dimension , 2003 .

[22]  Satoshi Uemura,et al.  Digital Media Information Base , 1999 .

[23]  Timothy K. Shih,et al.  Distributed Multimedia Databases: Techniques and Applications , 2001 .