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Reference TypeConference Paper (Conference Proceedings)
Last Update2005: (UTC) administrator
Metadata Last Update2020: (UTC) administrator
Citation KeyHirata:2005:BiImOp
TitleBinary image operator design based on stacked generalization
Access Date2021, Nov. 27
Number of Files1
Size506 KiB
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AuthorHirata, Nina Sumiko Tomita
AffiliationDepartment of Computer Science, Institute of Mathematics and Statistics, University of Sao Paulo
EditorRodrigues, Maria Andr?ia Formico
Frery, Alejandro C?sar
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)
Conference LocationNatal
Date9-12 Oct. 2005
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2008-07-17 14:11:00 :: nina -> banon ::
2008-08-26 15:17:02 :: banon -> administrator ::
2009-08-13 20:37:53 :: administrator -> banon ::
2010-08-28 20:01:19 :: banon -> administrator ::
2020-02-19 03:19:18 :: administrator -> :: 2005
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Content TypeExternal Contribution
Keywordsstacked generalization
image operator design
multi-stage training
AbstractStacked generalization refers to any learning schema that consists of multiple levels of training. Level zero classifiers are those that depend solely on input data while classifiers at other levels may use the output of lower levels as the input. Stacked generalization can be used to address the difficulties related to the design of image operators defined on large windows. This paper describes a simple stacked generalization schema for the design of binary image operators and presents several application examples that show its effectiveness as a training schema.
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