Optimizing support vector machine based classification and retrieval of semantic video events with genetic algorithms

Building accurate models for video event classification is an important research issue since they are essential components for effective video indexing and retrieval. Recently kernel-based methods, particularly support vector machines, have become popular in multimedia classification tasks. However, in order to use them effectively, several factors that hinder accurate classification results, such as feature subset selection and selection of the SVM kernel parameters, must be addressed through the use of heuristic-based techniques. We present a new approach to enhance the performance of SVM for video events classification based on a search method. The latter relies on the simultaneous optimization of the feature and instance subset and SVM kernel parameters, with genetic algorithms. Classification results on sport videos show the significant improvement over conventional SVM.