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Fast Supervised LDA for Discovering Micro-Events in Large-Scale Video Datasets

2016

Conference Paper


This paper introduces fsLDA, a fast variational inference method for supervised LDA, which overcomes the computational limitations of the original supervised LDA and enables its application in large-scale video datasets. In addition to its scalability, our method also overcomes the drawbacks of standard, unsupervised LDA for video, including its focus on dominant but often irrelevant video information (e.g. background, camera motion). As a result, experiments in the UCF11 and UCF101 datasets show that our method consistently outperforms unsupervised LDA in every metric. Furthermore, analysis shows that class-relevant topics of fsLDA lead to sparse video representations and encapsulate high-level information corresponding to parts of video events, which we denote "micro-events".

Author(s): Angelos Katharopoulos and Despoina Paschalidou and Christos Diou and Anastasios Delopoulos
Book Title: Proceedings of the 2016 ACM on Multimedia Conference
Pages: 332,336
Year: 2016
Month: October

Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

DOI: 10.1145/2964284.2967237
Event Name: ACM Multimedia Conference
Event Place: Amsterdam, The Netherlands

ISBN: 978-1-4503-3603-1
State: Published
URL: http://dl.acm.org/citation.cfm?id=2967237

Links: pdf
Project page
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Attachments: poster

BibTex

@inproceedings{katharopoulos2016fast,
  title = {Fast Supervised LDA for Discovering Micro-Events in Large-Scale Video Datasets},
  author = {Katharopoulos, Angelos and Paschalidou, Despoina and Diou, Christos and Delopoulos, Anastasios},
  booktitle = {Proceedings of the 2016 ACM on Multimedia Conference},
  pages = {332,336},
  month = oct,
  year = {2016},
  url = {http://dl.acm.org/citation.cfm?id=2967237},
  month_numeric = {10}
}