Chinese Paintings Sentiment Recognition via CNN Optimization with Human Cognition
SHENG Jiachuan1, CHEN Yaqi1, WANG Jun2, LI Liang3,4
1. School of Science and Technology, Tianjin University of Finance and Economics, Tianjin 300222; 2. Department of Management Information Systems, Tianjin University of Finance and Economics, Tianjin 300222; 3. Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002; 4. College of Intelligence and Computing, Tianjin University, Tianjin 300350
Abstract：Most existing methods are lack of analysis of Chinese paintings sentiment. In this paper, an algorithm of Chinese paintings sentiment recognition via convolutional neural network (CNN) optimization with human cognition is proposed. Firstly, the region of interest from painting is extracted to obtain the square area with rich emotional expression according to image saliency and brushstroke complexity. Then, the deep learning network structure is optimized by combining feature visualization and the knowledge of emotional expression techniques in traditional Chinese paintings. Finally, a rebuilt network is fine-tuned for Chinese paintings sentiment classification task. In the experiment, 1000 Chinese paintings are classified with four emotions and the accuracy rate is better than that of other convolutional neural networks. The effect of model operation is explained through ablation and visualization experiments, and thus the ability of the proposed algorithm to recognize the sentiment of traditional Chinese paintings is confirmed.
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2020, Vol. 33 
