Discrete Probability Distribution Prediction of Image Emotions with Shared Sparse Learning

作者：Zhao, Sicheng; Ding, Guiguang^*; Gao, Yue; Zhao, Xin; Tang, Youbao; Han, Jungong; Yao, Hongxun; Huang, Qingming

来源：IEEE Transactions on Affective Computing, 2020, 11(4): 574-587.

DOI：10.1109/TAFFC.2018.2818685

• Visualization
• Feature extraction
• Probability distribution
• Training
• Task analysis
• Dictionaries
• Learning systems
• Emotion distribution
• image emotions
• shared sparse learning
• multi-feature fusion

Computationally modelling the affective content of images has been extensively studied recently because of its wide applications in entertainment, advertisement, and education. Significant progress has been made on designing discriminative features to bridge the affective gap. Assuming that viewers can reach a consensus on the emotion of images, most existing works focused on assigning the dominant emotion category or the average dimension values to an image. However, the image emotions perceived by viewers are subjective by nature with the influence of personal and situational factors. In this paper, we propose a novel machine learning approach that characterizes the categorical image emotions as a discrete probability distribution (DPD). To associate emotion with the visual features extracted from images, we present shared sparse learning to learn the combination coefficients, with which the DPD of an unseen image is predicted by linearly combining the DPDs of the training images. Furthermore, we extend our method to the setup where multi-features are available and learn the optimal weights for each feature to reflect the importance of different features. Extensive experiments are carried out on Abstract, Emotion6 and IESN datasets and the results demonstrate the superiority of the proposed method, as compared to the state-of-the-art approaches.

• 出版日期2020-10-1
• 单位清华大学; 中国科学院大学; 哈尔滨工业大学