Degree of loop assessment in microvideo

Abstract

This paper presents a degree-of-loop assessment method for microvideo clips. Loop video is one of the popular features in microvideo, but there are so many non-loop video tagged with “loop” on microvideo services. This is because upload-ers or spammers also know that loop video is popular and they want to draw attention from viewers. In this paper, we statistically analyze the scene dynamics of the video by using color, optical flow, saliency maps, and evaluate the degree-of-loop. We have collected more than 1,000 video clips from Vine and subjectively evaluated their degree-of-loop. Experimental results show that our proposed algorithm can classify loop/non-loop video with 85.7% accuracy and categorize them into five degree-of-loop categories with 61.5% accuracy.