2026-09-19T15:41:59Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/42191 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Huang, Thomas S. Huang, Thomas S. Han, Jiawei Hasegawa-Johnson, Mark A. Liang, Zhi-Pei Tsai, Shen-Fu 2013-02-03T19:27:24Z 2013-02-03T19:27:24Z 2012-12 2013-02-03T19:27:24Z 2012-12 Lack of human prior knowledge is one of the main reasons that the semantic gap still remains when it comes to automatic multimedia understanding. One difference between the human cognition system and state-of-the-art machine vision algorithms is that the former possesses and uses high-level semantic knowledge, or ontology. In this thesis, we present our work on image-level annotation and album-level event recognition, both emphasizing the ontological structure among concepts including object, scene, and event. The inference and learning make use of mutual relations among these concepts, and are general for any concept and initial concept recognition. Our experiments show that the proposed frameworks are able to perform the respective visual recognition tasks better than other methods that are also based on middle-level recognition with or without ontology, and better than methods based purely on low-level features, thus validating the use of ontology in recognizing high-level and abstract concepts. Item withdrawn by Alexis Thompson (athmpsn1@illinois.edu) on 2012-08-06T14:17:16Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Tsai_ShenFu.pdf: 3271697 bytes, checksum: 741b86669b81d53a73f4b2474b1a0b39 (MD5) Made available in DSpace on 2013-02-03T19:27:24Z (GMT). No. of bitstreams: 2 Shen-Fu_Tsai.pdf: 3271697 bytes, checksum: 741b86669b81d53a73f4b2474b1a0b39 (MD5) license.txt: 4060 bytes, checksum: d0ee1fdbc024df6c97665fe8f1f5c590 (MD5) http://hdl.handle.net/2142/42191 en Copyright 2012 Shen-Fu Tsai Visual understanding ontology machine learning pattern recognition Toward ontological visual understanding text Electrical & Computer Eng 1933 Electrical & Computer Engr 1200 University of Illinois at Urbana-Champaign Dissertation Ph.D. PHD:Electr & Computer Eng-UIUC 10KS1200PHD