Unsupervised Reinforcement Learning For Video Summarization Reward Function

被引：6

作者：

Wang, Lei ^{[1
]}

Zhu, Yaping ^{[1
]}

Pan, Hong ^{[2
]}

机构：

[1] Commun Univ China, Sch Informat & Commun Engn, Beijing 100024, Peoples R China

[2] Southeast Univ, Sch Automat, Nanjing 210096, Jiangsu, Peoples R China

来源：

PROCEEDINGS OF 2019 INTERNATIONAL CONFERENCE ON IMAGE, VIDEO AND SIGNAL PROCESSING (IVSP 2019) | 2019年

基金：

中国国家自然科学基金;

关键词：

Video Summarization; Deep Reinforcement Learning; Convolutional Neural Network; Bi-directional Long Short-Term Memory; Deep Summarization Network;

D O I：

10.1145/3317640.3317658

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We propose a new reward function based on Deep Summarization Network (DSN), which is used to synthesize short video summaries to facilitate large-scale browsing of videos. The DSN uses the video summarization as a process of sequential decision making, predicting the probability of each video frame to indicate the likelihood that the video frame is selected, and then selecting the frame based on the probability distribution to form video summaries. By designing a new DSN reward function, the rewards for representative and diversity rewards are higher, and a large number of experiments are performed on the two benchmark datasets, demonstrating that our summary network is significantly better than existing unsupervised video summaries.

引用

页码：40 / 44

页数：5