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A multistrategy machine learning approach to object recognition

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Publication Type technical report
School or College College of Engineering
Department Computing, School of
Creator Ming, John C.
Title A multistrategy machine learning approach to object recognition
Date 1991-03
Description This work describes an innovative approach that combines machine learning and vision into an integrated system. The system is called ORACLE: Object Recognition Accomplished through Consolidation Learning Expertise. It uses two machine learning techniques known as explanation-based learning and conceptual clustering, combined in a synergistic manner, which provide effective object recognition. The learning system can automatically acquire new object models when unknown objects of interest are encountered in the environment. In addition, the object models are dynamic and qualitative in nature so they can be refined by the learning techniques during the recognition-learning cycle of the ORACLE system. Finally, the ORACLE system employs knowledge-based reasoning to effectively handle problems such as imprecise segmentation, image noise, and occlusion.
Type Text
Publisher University of Utah
Subject ORACLE; ORACLE system; computers
Subject LCSH Computer vision; Machine learning; Optical pattern recognition; Oracle (Computer file)
Language eng
Bibliographic Citation Ming, J. C. (1991). A multistrategy machine learning approach to object recognition. UUCS-91-05
Series University of Utah Computer Science Technical Report
Relation is Part of ARPANET
Format Medium application/pdf
Format Extent 157,203 Bytes
File Name Ming-A_Multistrategy_Machine.pdf
Conversion Specifications Original scanned with Kirtas 2400 and saved as 400 ppi uncompressed TIFF. PDF generated by Adobe Acrobat Pro X for CONTENTdm display
ARK ark:/87278/s60885dr
Setname ir_computersa
Date Created 2015-11-02
Date Modified 2015-11-03
ID 94876
Reference URL https://collections.lib.utah.edu/ark:/87278/s60885dr
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