Video classification in user profile generation for personalized broadcast services Automatic generation of user profiles, as specified in the MPEG7 user preference description scheme, for personized broadcast services is investigated in this work. Our research has focused on categorization of userfavored video into different semantically meaningful classes. This knowledge is then used in media filtering guidance and user preferred AV content selection. Several visual and motion features are extracted from source video sequences, such as the number of intracoded macroblocks, the macroblock motion information, temporal variances and shot activity histograms, for the classification purpose. Moreover, to further improve the accuracy of classification results, a fuzzy nearest prototype classifier is applied in this work. It is shown by experimental results that the proposed classification scheme is efficient and accurate.

Investigation of Automatic Video Summarization using Viewerxe2x80x99s Physiological, Facial and Attentional Features Video summarization aims at the selection of a concise and representative set of keyframes or video segments that allows the identification of the video content. In either cases, traditional summarization techniques usually work by segmenting the video into shots, representing video frames as feature vectors of color, texture, audio, among other features, clustering frames with similar features and selecting most representative keyframes or segments, sometimes guided by a videotosummary ratio target. The resulting summaries are typically subject independent and do not take into account specific viewerxe2x80x99s behavior. Instead of using intrinsic features extracted from the video for summarization, in this article we study whether personalized (subject dependent) video summaries can be obtained from physiological, facial, and attentional data captured from the viewers. More specifically, we study the relationship between personalized video summaries reported by viewers and their data captured during the display of different video genres. A dataset of fifteen videos was used in the experiments. During the exhibition of the videos, the viewerxe2x80x99s physiological, facial, and attentional data were recorded, analyzed and synchronized. Several machine learning models were trained to test our hypothesis. We obtained kfold cross validation accuracies that were above the chance for the best learned models. As a result of this study, we conclude that it is possible to train a learning machine that can produce customized summaries that are closer to user preferences compared to randomly produced summaries.