Chinese Paintings Emotion Recognition Based on End-to-end Weakly Supervised Learning Network Model - LU Ke-bin; YIN Shou-lin (Journal of Harbin University of Science and Technology)
Emotion recognition research is a hot spot in computer vision, and the study of Chinese painting emotion is of great significance to the appreciation of works. In order to improve the recognition performance, the traditional convolutional neural network used to extract local information of Chinese painting will lead to the loss of effective information. Therefore, the end-to-end weakly supervised learning network is proposed to recognize the Chinese painting emotion.The proposed learning network consists of two classification modules and one affective intensity prediction module.First, the intensity prediction flow is constructed on the basis of improved feature pyramid network to extract multi-level features.The gradient-based class activation map technique is used to generate pseudo-intensity maps from the first classification stream to guide the emotional intensity learning of the proposed network. The predicted intensity map is input into the second classification stream for the final Chinese painting emotion recognition. Finally, the proposed method is verified on the open data set.The experiment results show that the proposed network has improved the confounding matrix, average classification accuracy and average emotion recognition rate by 10%, 15% and 13% respectively.