Exploring Automatic Translation between Different Affect Representation Schemes

Exploring Automatic Translation between Different Affect Representation Schemes: Affective Image Content Analysis

LIU, SHUANG (TU Delft Electrical Engineering, Mathematics and Computer Science)

Dudzik, B.J.W. (mentor)
Raman, C.A. (mentor)
Hanjalic, A. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-06-29

Images possess the ability to convey a wide range of emotions, and extracting affective information from images is crucial for affect prediction systems. This process can be achieved through the application of machine learning algorithms. Categorical Emotion States (CES) and Dimensional Emotion Space (DES) are two typical models used for representing emotions. Moreover, the development of a mapping schema between these representations can contribute to benefit the research in AI and psychology. Consequently, this study focuses on investigating the feasibility of translating emotions from DES to CES. To accomplish this goal, relevant databases are identified and combined as the training data, and machine learning models, namely Naive Bayes, K-nearest Neighbors, and Decision Tree are employed to perform the classification task. The results indicate the superior performance of the K-nearest Neighbors classifier, exhibiting higher mean accuracy (60%) and low standard deviation (0.004) among all implemented classifiers. Overall, the translation from DES to CES offers several advantages, including a simplified and interpretable representation of emotions, as well as the provision of a common language for discussing and expressing emotions.

Computer Science
Human-Computer Interaction
Psychology

http://resolver.tudelft.nl/uuid:1b91df26-d2a4-4076-bb86-612c17229e41

Student theses

bachelor thesis

© 2023 SHUANG LIU