Li Jing, Chen Dongliang, Yu Ning, Zhao Ziping, Lv Zhihan
College of Art, Qingdao Agricultural University, Qingdao, China.
College of Computer Science and Technology, Qingdao University, Qingdao, China.
Front Psychol. 2021 Oct 22;12:741665. doi: 10.3389/fpsyg.2021.741665. eCollection 2021.
Today, with the rapid development of economic level, people's esthetic requirements are also rising, they have a deeper emotional understanding of art, and the voice of their traditional art and culture is becoming higher. The study expects to explore the performance of advanced affective computing in the recognition and analysis of emotional features of Chinese paintings at the 13th National Exhibition of Fines Arts. Aiming at the problem of "semantic gap" in the emotion recognition task of images such as traditional Chinese painting, the study selects the AlexNet algorithm based on convolutional neural network (CNN), and further improves the AlexNet algorithm. Meanwhile, the study adds chi square test to solve the problems of data redundancy and noise in various modes such as Chinese painting. Moreover, the study designs a multimodal emotion recognition model of Chinese painting based on improved AlexNet neural network and chi square test. Finally, the performance of the model is verified by simulation with Chinese painting in the 13th National Exhibition of Fines Arts as the data source. The proposed algorithm is compared with Long Short-Term Memory (LSTM), CNN, Recurrent Neural Network (RNN), AlexNet, and Deep Neural Network (DNN) algorithms from the training set and test set, respectively, The emotion recognition accuracy of the proposed algorithm reaches 92.23 and 97.11% in the training set and test set, respectively, the training time is stable at about 54.97 s, and the test time is stable at about 23.74 s. In addition, the analysis of the acceleration efficiency of each algorithm shows that the improved AlexNet algorithm is suitable for processing a large amount of brain image data, and the acceleration ratio is also higher than other algorithms. And the efficiency in the test set scenario is slightly better than that in the training set scenario. On the premise of ensuring the error, the multimodal emotion recognition model of Chinese painting can achieve high accuracy and obvious acceleration effect. More importantly, the emotion recognition and analysis effect of traditional Chinese painting is the best, which can provide an experimental basis for the digital understanding and management of emotion of quintessence.
如今，随着经济水平的快速发展，人们的审美需求也在不断提高，他们对艺术有了更深刻的情感理解，传统艺术文化的呼声也越来越高。本研究期望在第十三届全国美术展览中探索先进情感计算在中国画情感特征识别与分析中的表现。针对中国画等图像情感识别任务中存在的“语义鸿沟”问题，本研究选用基于卷积神经网络（CNN）的AlexNet算法，并对AlexNet算法进行进一步改进。同时，本研究添加卡方检验以解决中国画等多种模式下的数据冗余和噪声问题。此外，本研究设计了一种基于改进AlexNet神经网络和卡方检验的中国画多模态情感识别模型。最后，以第十三届全国美术展览中的中国画为数据源进行仿真验证该模型的性能。将所提算法分别与长短期记忆网络（LSTM）、卷积神经网络（CNN）、循环神经网络（RNN）、AlexNet和深度神经网络（DNN）算法在训练集和测试集上进行比较，所提算法在训练集和测试集上的情感识别准确率分别达到92.23%和97.11%，训练时间稳定在约54.97秒，测试时间稳定在约23.74秒。此外，各算法加速效率分析表明，改进后的AlexNet算法适用于处理大量脑图像数据，加速比也高于其他算法。且在测试集场景下的效率略优于训练集场景。在保证误差的前提下，中国画多模态情感识别模型能够实现较高的准确率和明显的加速效果。更重要的是，对传统中国画的情感识别与分析效果最佳，可为国粹情感的数字化理解与管理提供实验依据。
