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Improving Sequential Determinantal Point Processes for Supervised Video Summarization
Book chapter   Open access   Peer reviewed

Improving Sequential Determinantal Point Processes for Supervised Video Summarization

Aidean Sharghi, Ali Borji, Chengtao Li, Tianbao Yang and Boqing Gong
Computer Vision – ECCV 2018, pp.533-550
Lecture Notes in Computer Science, Springer International Publishing
10/07/2018
DOI: 10.1007/978-3-030-01219-9_32
url
https://stars.library.ucf.edu/scopus2015/10118View
Open Access

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.
 Determinantal Point Process (DPP) Egocentric Video Exposure Bias Generalized DPP (GDPP) Large Margin Algorithm

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