Reference TypeConference Proceedings
Citation KeyHirata:2005:BiImOp
AuthorHirata, Nina Sumiko Tomita
AffiliationDepartment of Computer Science, Institute of Mathematics and Statistics, University of Sao Paulo
TitleBinary image operator design based on stacked generalization
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)
EditorRodrigues, Maria Andr?ia Formico
Frery, Alejandro C?sar
Book TitleProceedings
Date9-12 Oct. 2005
Publisher CityLos Alamitos
PublisherIEEE Computer Society
Conference LocationNatal
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.
Tertiary TypeFull Paper
Size506 KiB
Number of Files1
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Last Update2005: administrator
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