Full metadata
Title
Diversity Promoting Online Sampling for Streaming Video Summarization
Description
Video summarization is gaining popularity in the technological culture, where positioning the mouse pointer on top of a video results in a quick overview of what the video is about. The algorithm usually selects frames in a time sequence through systematic sampling. Invariably, there are other applications like video surveillance, web-based video surfing and video archival applications which can benefit from efficient and concise video summaries. In this project, we explored several clustering algorithms and how these can be combined and deconstructed to make summarization algorithm more efficient and relevant. We focused on two metrics to summarize: reducing error and redundancy in the summary. To reduce the error online k-means clustering algorithm was used; to reduce redundancy we applied two different methods: volume of convex hulls and the true diversity measure that is usually used in biological disciplines. The algorithm was efficient and computationally cost effective due to its online nature. The diversity maximization (or redundancy reduction) using technique of volume of convex hulls showed better results compared to other conventional methods on 50 different videos. For the true diversity measure, there has not been much work done on the nature of the measure in the context of video summarization. When we applied it, the algorithm stalled due to the true diversity saturating because of the inherent initialization present in the algorithm. We explored the nature of this measure to gain better understanding on how it can help to make summarization more intuitive and give the user a handle to customize the summary.
Date Created
2017-05
Contributors
- Masroor, Ahnaf (Co-author)
- Anirudh, Rushil (Co-author)
- Turaga, Pavan (Thesis director)
- Spanias, Andreas (Committee member)
- School of Mathematical and Statistical Sciences (Contributor)
- Electrical Engineering Program (Contributor)
- Barrett, The Honors College (Contributor)
Topical Subject
Extent
16 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Series
Academic Year 2016-2017
Handle
https://hdl.handle.net/2286/R.I.42672
Level of coding
minimal
Cataloging Standards
System Created
- 2017-10-30 02:50:58
System Modified
- 2021-08-11 04:09:57
- 3 years 3 months ago
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