[1]WANG Xingrun,NIE Xiushan,YANG Fan,et al.Video summarization based on learning to rank[J].CAAI Transactions on Intelligent Systems,2018,13(6):921-927.[doi:10.11992/tis.201806013]
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Video summarization based on learning to rank

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 6 Page number: 921-927 Column: 学术论文—机器学习 Public date: 2018-10-25
Title:
Video summarization based on learning to rank
Author(s):
WANG Xingrun1;  NIE Xiushan2;  YANG Fan2;  LYU Peng2;  YIN Yilong3
1. School of Computer Science and Technology, Shandong University, Ji’nan 250101, China;
2. School of Computer Science and Technology, Shandong University of Finance and Economics, Ji’nan 250014, China;
3. School of Software Engineering, Shandong University, Ji’nan 250101, China
Keywords:
video frame; summary; video frame grabbers; ranking; video operation; video images; video; deep learning
CLC:
TP389.1
DOI:
10.11992/tis.201806013
Abstract:
The exponential increase in the number of online videos has resulted in several challenges as regards video browsing, video storing, and video retrieval. These challenges can be effectively solved by video summarization. The existing video summarization methods construct objective functions based on empirical constraints and experience setup resulting from scoring for a set of frames. However, these methods have uncertainty and high complexity; therefore, in this paper, a video summarization method based on learning-to-rank algorithm is proposed. The proposed method considers summary extraction as a correlation ranking problem between frames and video. First, the training set is used to learn the ranking function, which places the frames having high correlation with video in the front position. Then, the score of each frame is calculated using the learned ranking function. Finally, the keyframes with high scores are selected as the video summary. Compared with the existing methods, the proposed method calculates a score for each frame rather than for a set of frames; therefore, computation complexity remarkably decreases. In addition, the effectiveness of the proposed approach is validated using experimental results on TVSum50 dataset.
References:
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