User generated video annotation using geo-tagged image databases
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Measuring bitrate and quality trade-off in a fast region-of-interest based video coding
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
Saliency-based image editing for guiding visual attention
Proceedings of the 1st international workshop on pervasive eye tracking & mobile eye-based interaction
Sensor-based analysis of user generated video for multi-camera video remixing
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Semantic scalability using tennis videos as examples
Multimedia Tools and Applications
Proceedings of the 4th ACM Multimedia Systems Conference
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In this paper we propose a system for the analysis of user generated video (UGV). UGV often has a rich camera motion structure that is generated at the time the video is recorded by the person taking the video, i.e., the ??camera person.?? We exploit this structure by defining a new concept known as camera view for temporal segmentation of UGV. The segmentation provides a video summary with unique properties that is useful in applications such as video annotation. Camera motion is also a powerful feature for identification of keyframes and regions of interest (ROIs) since it is an indicator of the camera person's interests in the scene and can also attract the viewers' attention. We propose a new location-based saliency map which is generated based on camera motion parameters. This map is combined with other saliency maps generated using features such as color contrast, object motion and face detection to determine the ROIs. In order to evaluate our methods we conducted several user studies. A subjective evaluation indicated that our system produces results that is consistent with viewers' preferences. We also examined the effect of camera motion on human visual attention through an eye tracking experiment. The results showed a high dependency between the distribution of fixation points of the viewers and the direction of camera movement which is consistent with our location-based saliency map.