ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A tutorial on support vector regression
Statistics and Computing
Automatic Face Recognition for Film Character Retrieval in Feature-Length Films
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Movie/Script: Alignment and Parsing of Video and Text Transcription
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Weakly-Supervised Violence Detection in Movies with Audio and Video Based Co-training
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Video scene segmentation using Markov chain Monte Carlo
IEEE Transactions on Multimedia
Modeling social strength in social media community via kernel-based learning
MM '11 Proceedings of the 19th ACM international conference on Multimedia
From the internet of things to embedded intelligence
World Wide Web
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If you have ever watched movies or television shows, you know how easy it is to tell the good characters from the bad ones. Little, however, is known "whether" or "how" computers can achieve such high-level understanding of movies. In this paper, we take the first step towards learning the relations among movie characters using visual and auditory cues. Specifically, we use support vector regression to estimate local characterization of adverseness at the scene level. Such local properties are then synthesized via statistical learning based on Gaussian processes to derive the affinity between the movie characters. Once the affinity is learned, we perform social network analysis to find communities of characters and identify the leader of each community. We experimentally demonstrate that the relations among characters can be determined with reasonable accuracy from the movie content.