-action by computing the visual disturbance feature of every movie. Vi-sual disturbance is defined as a measure of motion content in a clip. Next we use color, audio and cinematic principles for further classification into comedy, horror, drama/other and movies containing explosions and gunfire. Potential ap

Abstract. To detect violence in movies, we present a three-stage method inte-grating visual and auditory cues. In our method, those shots with potential vio-lent content are first identified according to universal film-making rules. A modified semi-supervised learning technique based on semi

with comparative results provided across modalities. The produced summaries, based on low-level features and content-independent fusion and selection, are of subjectively high aesthetic and informative quality. Index Terms—Attention, audio saliency, fusion, movie summarization, multimodal saliency, multistream

We describe a recommender system which uses a unique combination of content-based and collaborative methods to suggest items of interest to users, and also to learn and exploit item semantics. Recommender systems typically use tech-niques from collaborative filtering, in which proximity mea

on (1) collaborative data encoding which users like which items, and/or (2) content data describing item features and user demographics. Systems that rely solely on collaborative data fail when operating from a cold start|that is, when recommending items (e.g., rst-run movies) that no member

their recommendations, in an effort to regain customer acceptance and trust. However, their explanations are not sufficient, because they are based solely on rating or navigational data, ignoring the content data. Several systems have proposed the combination of content data with rating data to provide more accurate

-uate two different algorithms, namely a content-based algo-rithm and an approach that exploits social similarity. While the former is based on rhythm features, the latter exploits “related videos ” relations from YouTube. We show via a user questionnaire that recommendation results based on content

This paper describes an original approach for motion characterization with a view to content-based video indexing and retrieval. A statistical analysis of temporal cooccurrence distributions of relevant local motion-based measures is exploited to compute global motion descriptors, which allows

Logical feature population of visual databases exploits the concept that credible locations of feature can be determined to a high degree of probability if a set of rules can be defined to allow such predictions. This paper describes a method for augmenting feature content of existing urban feature

We propose an audio-visual approach to video genre classification using content descriptors that exploit audio, color, temporal, and contour information. Audio information is extracted at block-level, which has the advantage of capturing local temporal information. At the temporal structure level