A Novel Horror Scene Detection Scheme On Revised Multiple Instance Learning Model

Horror scene detection is a research problem that has much practical use. The supervised method requires the training data to be labeled manually, which can be tedious and onerous. In this paper, a more challenging setting of the problems without complete information on data labels is investigated. In particular, as the horror scene is characterized by multiple features, this problem is formulated as a special multiple instance learning (MIL) problem - Multiple Grouped Instance Learning (MGIL), which requires partial labeled training. To solve the MGIL problem, a learning method is proposed Multiple Distance- Expectation Maximization Diversity Density (MD-EMDD). Additionally, a survey is conducted to collect people's opinions based on the definition of horror scenes. Combined with the survey results, Labeled with Ranking - MD - EMDD is proposed and demonstrated better results when compared to the traditional MIL algorithm and close to performance achieved by supervised method.