MACHINE PERCEPTION AND ROBOTICS GROUP

Dept. of Robotics Science and Technology,
Chubu University

We analyze the tendencies in choosing cut-edit points in personal video content edited by users on the Internet, and develop a method to automatically estimate cut-edit points based on the results. When we investigated the relationship among cut-edit points in personal videos using a space-time patch feature(ST-patch feature), we realized that cut-edits were done in frames with a low Continuous Rank-Increase Measure (CRIM) value and a high Motion Correlation (MC) value calculated from the ST-patch feature. Therefore, we propose a method for estimating cut-edit points based on CRIM and MC values. Experimental results indicate that we obtained a recall ratio of 61.3% and a precision ratio of 50.4%.