A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Principled Approach to Detecting Surprising Events in Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Particle Video: Long-Range Motion Estimation using Point Trajectories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Video retargeting: automating pan and scan
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Automatically protecting privacy in consumer generated videos using intended human object detector
Proceedings of the international conference on Multimedia
Object segmentation by long term analysis of point trajectories
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Learning to Detect a Salient Object
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling and Mining of Users' Capture Intention for Home Videos
IEEE Transactions on Multimedia
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When camera persons take videos with mobile video cameras, they usually have capture intentions, i.e., what they want to express in their videos, and there are intentionally captured regions (ICRs) in the video frames that are essential for the capture intentions. Extracting ICRs is thus beneficial for wide range of applications such as video summarization and video adaptation for small displays. In this paper, we present a novel method for automatically extracting ICRs. A camera person usually moves his/her camera so that ICRs can be arranged in appropriate positions in video frames; therefore, ICRs can yield specific motion. This observation indicates that such specific motion is a vital cue for extracting ICRs. The proposed method represents motion by point trajectories, which are long-term trajectories of spatially dense points in video frames, and extracts ICRs using an ICR model based on the point trajectories. We experimentally evaluate the proposed method to demonstrate its potential applicability.