Affective Content Detection by Using Timing Features and Fuzzy Clustering

Abstract

Emotional factors directly reflect audiences’ attention, evaluation and memory. Movie affective content detection attracts more and more research efforts. Most of the existing work focus on developing efficient affective features or implementing feasible pattern recognition algorithms. However, some important issues are ignored. 1) Most of the feature used in affective content detection are traditional visual/audio features. While affective content detection needs those features which are directly related to emotions. 2) affective content is a subjective concept which heavily depends on human perception. It is hard to find a clear boundary for various emotion categories. While most of the existing methods utilize hard pattern recognition algorithm to generate clear boundary for emotion categories. In this paper, we consider the above two issues by two aspects. 1) We employ timing features which are important of films and an important part of films’ power to affect viewers’ feelings and emotions. Meanwhile, audio features are used together with timing features to detect affective content from multiple modalities. 2) Fuzzy clustering is used in this paper to map affective features to affective content. Fuzzy logic provides a mathematical model to represent vagueness, which is close to human perception. Experimental results shows the proposed method is effective and efficient.