Self-adaptive harmony search algorithm for optimization
Expert Systems with Applications: An International Journal
IEEE Transactions on Audio, Speech, and Language Processing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Overview of the MPEG-7 standard
IEEE Transactions on Circuits and Systems for Video Technology
On the use of computable features for film classification
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we propose a movie genre classification system using a meta-heuristic optimization algorithm called Self-Adaptive Harmony Search (i.e., SAHS) to select local features for corresponding movie genres. Then, each one-against-one Support Vector Machine (i.e., SVM) classifier is fed with the corresponding local feature set and the majority voting method is used to determine the prediction of each movie. Totally, we extract 277 features from each movie trailer, including visual and audio features. However, no more than 25 features are used to discriminate each pair of movie genres. The experimental results show that the overall accuracy reaches 91.9%, and this demonstrates more precise features can be selected for each pair of genres to get better classification results.