A new SVM based emotional classification of image

How high-level emotional representation of art paintings can be inferred from perceptual level features suited for the particular classes (dynamic vs. static classification) is presented. The key points are feature selection and classification. According to the strong relationship between notable lines of image and human sensations, a novel feature vector WLDLV (Weighted Line Direction-Length Vector) is proposed, which includes both orientation and length information of lines in an image. Classification is performed by SVM (Support Vector Machine) and images can be classified into dynamic and static. Experimental results demonstrate the effectiveness and superiority of the algorithm.

[1]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[2]  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).

[3]  Stéphane Mallat,et al.  Characterization of Signals from Multiscale Edges , 2011, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Anil K. Jain,et al.  On image classification: city vs. landscape , 1998, Proceedings. IEEE Workshop on Content-Based Access of Image and Video Libraries (Cat. No.98EX173).

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

[6]  Alberto Del Bimbo,et al.  Semantics in Visual Information Retrieval , 1999, IEEE Multim..

[7]  Martin Szummer,et al.  Indoor-outdoor image classification , 1998, Proceedings 1998 IEEE International Workshop on Content-Based Access of Image and Video Database.

[8]  Christine L. Lisetti,et al.  Modeling Multimodal Expression of User’s Affective Subjective Experience , 2002, User Modeling and User-Adapted Interaction.