Improving Sequential Determinantal Point Processes For Supervised Video Summarization

Abstract

It is now much easier than ever before to produce videos. While the ubiquitous video data is a great source for information discovery and extraction, the computational challenges are unparalleled. Automatically summarizing the videos has become a substantial need for browsing, searching, and indexing visual content. This paper is in the vein of supervised video summarization using sequential determinantal point processes (SeqDPPs), which models diversity by a probabilistic distribution. We improve this model in two folds. In terms of learning, we propose a large-margin algorithm to address the exposure bias problem in SeqDPP. In terms of modeling, we design a new probabilistic distribution such that, when it is integrated into SeqDPP, the resulting model accepts user input about the expected length of the summary. Moreover, we also significantly extend a popular video summarization dataset by (1) more egocentric videos, (2) dense user annotations, and (3) a refined evaluation scheme. We conduct extensive experiments on this dataset (about 60 h of videos in total) and compare our approach to several competitive baselines.

Publication Date

1-1-2018

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

11207 LNCS

Number of Pages

533-550

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-030-01219-9_32

Socpus ID

85055125628 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85055125628

This document is currently not available here.

Share

COinS