2026-09-20T21:33:58Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/81814 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Sylvian Ray Oezer, Tuna 2015-09-25T20:20:33Z 2015-09-25T20:20:33Z 10000-01-01 2008 2008 The 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. Made available in DSpace on 2015-09-25T20:20:33Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3314861.pdf: 2492640 bytes, checksum: 7e0451f15a07ed93e3668785b11cc16a (MD5) Previous issue date: 2008 Embargo set by: Seth Robbins for item 83095 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 143 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008. http://hdl.handle.net/2142/81814 (MiAaPQ)AAI3314861 eng Computer Science Discovering Audio-Visual Associations in Narrated Videos of Human Activities text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.