Abstract
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.
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Supported by the National Natural Science Foundation of China (No.60372068)
Communication author: Wang Weining, born in 1975, female, Ph.D. student. College of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China.
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Wang, W., Yu, Y. & Zhang, J. A new SVM based emotional classification of image. J. of Electron.(China) 22, 98–104 (2005). https://doi.org/10.1007/BF02687959
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DOI: https://doi.org/10.1007/BF02687959