SVM-Based decision fusion model for detecting concepts in films

This paper studies a support vector machine (SVM) to obtain a decision fusion algorithm for detection of semantic concepts in videos, and its application to films database. Given a movie clip, its spatio-temporal information is captured by audiovisual features. These are then independently inputted to the corresponding matching experts whose outputs are fused at the decision stage by the SVM classifier. Based on our simulation results, this fusion method can attain very high recognition accuracy for detection of various concepts from a collection of Hollywood movies. It requires a very small set of training samples from a large database.