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oai:www.ideals.illinois.edu:2142/80809 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Huang, Thomas S. Zhou, Xiang Sean 2015-09-25T20:08:16Z 2015-09-25T20:08:16Z 10000-01-01 2002 2002 This dissertation deals with research topics in the area of content-based access of image and video data. The main objective is to bridge the semantic gap between high-level concepts in the human mind and low-level features extractable by the machines. An emphasis is put on the learning and classification aspect during the interactive retrieval process, namely, relevance feedback algorithms. A novel algorithm, BiasMap, is proposed to take into account specifically the small sample asymmetric nature of the problem. A kernel and a boosting approach have been applied for achieving nonlinear capability. Other research efforts include local and global structural representations for images to capture more semantic information, the mixed use of textual and low-level features to facilitate intelligent access and user interaction, and content-based, nonlinearly sampled video delivery over low-bit-rate channels. Made available in DSpace on 2015-09-25T20:08:16Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3070497.pdf: 6230215 bytes, checksum: 9cbbf66dec3384bb864b8fef0e24b5ff (MD5) Previous issue date: 2002 Embargo set by: Seth Robbins for item 82091 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 120 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2002. http://hdl.handle.net/2142/80809 (MiAaPQ)AAI3070497 eng Computer Science Content -Based Access of Image and Video Data text Electrical Engineering Electrical Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.