Emotion categorization based on probabilistic Latent Emotion Analysis model

This paper proposes a probabilistic Latent Emotion Analysis (pLEA) model, which is an extension of probabilistic Latent Semantic Analysis (pLSA) model, to discover latent emotion topics considering theoretical and empirical concepts from psychology and art theory. The traditional pLSA models performed poorly since the statistical model are hardly narrowing the semantic gap between low-level image features and high-level emotion concepts. To address this problem, this paper introduces the affective concept from art theory to bridge the semantic gap. To be specific, the pLEA model first builds the relations between visual words and affective concepts. And then discovers the latent emotion topics based on the affective concepts. Meanwhile, we exploit the training images labels to modulate the learning process of latent emotion topics, aiming to make them similar to the actual image emotion labels. Once obtained the emotion topics, the appearing frequency of the corresponding emotion topics in a given image forms a histogram, which can be subsequently used in the scene categorization task via the Support Vector Machine (SVM) classifier. The proposed approach has been tested on the International Affective Picture System (IAPS) and a set of artistic photography and abstract paintings. The experimental results have demonstrated the effectiveness of the proposed method for eliciting image emotions.