.1 Video segmentation The goal is to segment a movie into shots and to select a representative key frame from each shot. IBM’s Multimedia Analysis and Retrieval System (IMARS) (Natsev, Smith, Tešié, Xie, & Yan, 2008) will be used for shot boundary detection. Since a key frame can represent a shot, the middle frame from each shot will be extracted as key frame for visual analysis. 3.1.2 Feature Extraction Given the segmented clips, features are extracted in terms of actor appearance, genre, and visual descriptors. Actor appearance Actors are key to a consumer’s expectations of the movie. A good personalized trailer would feature those actors that are most relevant to a user’s interests. To recognize these actors, the easiest way is to use face recognition. A face recognition system using Eigenfaces (Turk & Pentland, 1991) will be implemented in OpenCV . Facial recognition using Eigenfaces promises great recognition accuracy of around 95% (Kannan et al., 2015). Genre Specific movie events can correspond to genres, i.e., a romantic shot in a movie should be classified as belonging to the romance genre, so that it is more likely to be recommended to someone who prefers romantic movies. These movie events are to be manually annotated for each shot as they cannot be automatically detected even using the most modern semantic concept detection methodologies (Kannan et al., 2015). This is because of the highly subjective nature of these movie events, and because “the low-level visual features trained for classification are not highly correlated with the corresponding event” (Kannan et al., 2015). Visual descriptors To match the available dataset, visual descriptions from the FC7 layer of the AlexNet convolutional neural network will be used. These represent abstract, top-level features that are discovered in each key frame, and are descriptors of color and texture. 3.3 Training process>