2026-09-19T01:48:03Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/81142 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Ahuja, Narendra Loeff, Nicolas 2015-09-25T20:09:44Z 2015-09-25T20:09:44Z 10000-01-01 2009 2009 To conclude, we interpret the internal representation of the model and use it to perform unsupervised scene discovery. Defining a meaningful vocabulary for scene discovery is a challenging problem that has important consequences for object recognition. We consider scenes to depict correlated objects and present visual similarity. We postulate that the internal representation space of our model should allow us to discover a large number of scenes in unsupervised data; we show scene discrimination results on par with supervised approaches even without explicitly labeling scenes, producing highly plausible scene clusters. Made available in DSpace on 2015-09-25T20:09:44Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3392199.pdf: 2072812 bytes, checksum: ab5d83cd854069ee6c6ee8f24aafd328 (MD5) Previous issue date: 2009 Embargo set by: Seth Robbins for item 82424 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 73 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009. http://hdl.handle.net/2142/81142 (MiAaPQ)AAI3392199 eng Engineering, Electronics and Electrical A New Framework for Semisupervised, Multitask Learning text Electrical and Computer Engineering Electrical and Computer Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.