Using context saliency for movie shot classification

Movie shot classification is vital but challenging task due to various movie genres, different movie shooting techniques and much more shot types than other video domain. Variety of shot types are used in movies in order to attract audiences attention and enhance their watching experience. In this pa per, we introduce context saliency to measure visual attention distributed in keyframes for movie shot classification. Different from traditional saliency maps, context saliency map is generated by removing redundancy from contrast saliency and incorporating geometry constrains. Context saliency is later combined with color and texture features to generate feature vectors. Support Vector Machine (SVM) is used to classify keyframes into pre-defined shot classes. Different from the existing works of either performing in a certain movie genre or classifying movie shot into limited directing semantic classes, the proposed method has three unique features: 1) context saliency significantly improves movie shot classification; 2) our method works for all movie genres; 3) our method deals with the most common types of video shots in movies. The experimental results indicate that the proposed method is effective and efficient for movie shot classification.