RoleNet: treat a movie as a small society
Proceedings of the international workshop on Workshop on multimedia information retrieval
Multi-modality web video categorization
Proceedings of the international workshop on Workshop on multimedia information retrieval
Hierarchical movie affective content analysis based on arousal and valence features
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Affective ranking of movie scenes using physiological signals and content analysis
MS '08 Proceedings of the 2nd ACM workshop on Multimedia semantics
Large scale incremental web video categorization
WSMC '09 Proceedings of the 1st workshop on Web-scale multimedia corpus
RoleNet: movie analysis from the perspective of social networks
IEEE Transactions on Multimedia - Special issue on integration of context and content
A movie classifier based on visual features
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
A semantic framework for video genre classification and event analysis
Image Communication
SceneMaker: automatic visualisation of screenplays
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Genre-specific semantic video indexing
Proceedings of the ACM International Conference on Image and Video Retrieval
Movie genre classification via scene categorization
Proceedings of the international conference on Multimedia
SceneMaker: multimodal visualisation of natural language film scripts
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part IV
Using scripts for affective content retrieval
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Adaptive local hyperplanes for MTV affective analysis
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
Multimedia Tools and Applications
SceneMaker: intelligent multimodal visualization of natural language scripts
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
Multi-actor emotion recognition in movies using a bimodal approach
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
High level video temporal segmentation
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Example-based video remixing support system
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Affective content analysis of music video clips
MIRUM '11 Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Movie genre classification using SVM with audio and video features
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Video Segmentation and Structuring for Indexing Applications
International Journal of Multimedia Data Engineering & Management
Video genre classification using weighted kernel logistic regression
Advances in Multimedia - Special issue on Multimedia Applications for Smart Device in Ubiquitous Environments
Film segmentation and indexing using autoassociative neural networks
International Journal of Speech Technology
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This work presents a framework for the classification of feature films into genres, based only on computable visual cues. We view the work as a step toward high-level semantic film interpretation, currently using low-level video features and knowledge of ubiquitous cinematic practices. Our current domain of study is the movie preview, commercial advertisements primarily created to attract audiences. A preview often emphasizes the theme of a film and hence provides suitable information for classification. In our approach, we classify movies into four broad categories: Comedies, Action, Dramas, or Horror films. Inspired by cinematic principles, four computable video features (average shot length, color variance, motion content and lighting key) are combined in a framework to provide a mapping to these four high-level semantic classes. Mean shift classification is used to discover the structure between the computed features and each film genre. We have conducted extensive experiments on over a hundred film previews and notably demonstrate that low-level visual features (without the use of audio or text cues) may be utilized for movie classification. Our approach can also be broadened for many potential applications including scene understanding, the building and updating of video databases with minimal human intervention, browsing, and retrieval of videos on the Internet (video-on-demand) and video libraries.