Evolutionary optimization of video event classification

Detection and classification of semantic events in video documents have been major tasks in the domain of automatic video analysis. The multimodality of video data brings about challenging issues and the effectiveness of its automatic semantic processing has been hampered by the video semantic gap, that is, the gap between low-level visual and spatio-temporal features used as a digital representation of video documents and their semantic interpretation. Traditionally, these low-level features are concatenated in high-dimensional spaces and classified as high-level semantic events through computational learning methods. Support vector machines (SVMs), widely used in the literature, have often shown to outperform other popular learning techniques for this task. However, concatenation of several features with different intrinsic properties and wide dynamical ranges may result in curse of dimensionality and redundancy issues. Among the factors impacting the effectiveness of classification are (i) feature subset selection, (ii) tuning of classifiers' parameters and (iii) selection of proper training instances. In this paper, we address these factors and propose a technique (denoted as GAoptSVM) for an optimal SVM-based video event classification through the use of an evolutionary optimization technique. Extensive experiments on the 50 h video data set of TRECVid 2008 event detection task and large quantities of video data collected from Youtube and CMU Graphics Lab Motion Capture Database demonstrate that our approach outperforms the traditional SVM and effectively classify video events with noticeable accuracy.