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University of Illinois at Urbana-Champaign
Discovering Audio-Visual Associations in Narrated Videos of Human Activities
Abstract
dc:descriptionThe experimental results show that the algorithm presented in this dissertation successfully discovers the correct associations between video scenes and audio utterances in an unsupervised way despite the imperfect correlation between the video and audio. The algorithm outperforms standard supervised learning algorithms. Among other things, this research shows that the performance of the algorithm depends mainly on the strength of the correlation between video and audio, the length of the narration associated with each video scene and the total number of words in the language.
Degree
thesis:*- Name thesis:degree_name
- Ph.D.
- Level thesis:degree_level
- Dissertation
- Discipline thesis:degree_discipline
- Computer Science
- Grantor
- University of Illinois at Urbana-Champaign
- Year dc:date
- 2015
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Oezer, Tuna
- Contributors dc:contributor
-
- Sylvian Ray
Subjects
dc:subject × 1Rights
- Language dc:language
- eng
Identifiers
dc:identifier.*- Identifier
- (MiAaPQ)AAI3314861
- OAI identifier oai:identifier
- oai:www.ideals.illinois.edu:2142/81814