Behaviour-Based Clustering of Neural Networks Applied to Document Enhancement
Identifieur interne : 000E86 ( Main/Exploration ); précédent : 000E85; suivant : 000E87Behaviour-Based Clustering of Neural Networks Applied to Document Enhancement
Auteurs : F. Zamora-Martínez [Espagne] ; S. Espa A-Boquera [Espagne] ; J. Castro-Bleda [Espagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2007.
Abstract
Abstract: This work proposes an agglomerative hierarchical clustering algorithm where the items to be clustered are supervised-learning classifiers. The measure of similarity to compare classifiers is based on their behaviour. This clustering algorithm has been applied to document enhancement: A set of neural filters is trained with multilayer perceptrons for different types of noise and then clustered into groups to obtain a reduced set of neural clustered filters. In order to automatically determine which clustered filter is the most suitable to clean and enhance a real noisy image, an image classifier is also trained using multilayer perceptrons.
Url:
DOI: 10.1007/978-3-540-73007-1_18
Affiliations:
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Zamora-Martínez</name><affiliation wicri:level="1"><country xml:lang="fr">Espagne</country><wicri:regionArea>Departamento de Lenguajes y Sistemas Informáticos, Universitat Jaume I, Castellón</wicri:regionArea><wicri:noRegion>Castellón</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Espagne</country></affiliation></author><author><name sortKey="Espa A Boquera, S" sort="Espa A Boquera, S" uniqKey="Espa A Boquera S" first="S." last="Espa A-Boquera">S. Espa A-Boquera</name><affiliation wicri:level="1"><country xml:lang="fr">Espagne</country><wicri:regionArea>Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Valencia</wicri:regionArea><wicri:noRegion>Valencia</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Espagne</country></affiliation></author><author><name sortKey="Castro Bleda, J" sort="Castro Bleda, J" uniqKey="Castro Bleda J" first="J." last="Castro-Bleda">J. Castro-Bleda</name><affiliation wicri:level="1"><country xml:lang="fr">Espagne</country><wicri:regionArea>Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Valencia</wicri:regionArea><wicri:noRegion>Valencia</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Espagne</country></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2007</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">4162B54BF1ED99D7D8BEFC39F2574BAB886B1BD5</idno><idno type="DOI">10.1007/978-3-540-73007-1_18</idno><idno type="ChapterID">18</idno><idno type="ChapterID">Chap18</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: This work proposes an agglomerative hierarchical clustering algorithm where the items to be clustered are supervised-learning classifiers. The measure of similarity to compare classifiers is based on their behaviour. This clustering algorithm has been applied to document enhancement: A set of neural filters is trained with multilayer perceptrons for different types of noise and then clustered into groups to obtain a reduced set of neural clustered filters. In order to automatically determine which clustered filter is the most suitable to clean and enhance a real noisy image, an image classifier is also trained using multilayer perceptrons.</div></front></TEI><affiliations><list><country><li>Espagne</li></country></list><tree><country name="Espagne"><noRegion><name sortKey="Zamora Martinez, F" sort="Zamora Martinez, F" uniqKey="Zamora Martinez F" first="F." last="Zamora-Martínez">F. Zamora-Martínez</name></noRegion><name sortKey="Castro Bleda, J" sort="Castro Bleda, J" uniqKey="Castro Bleda J" first="J." last="Castro-Bleda">J. Castro-Bleda</name><name sortKey="Castro Bleda, J" sort="Castro Bleda, J" uniqKey="Castro Bleda J" first="J." last="Castro-Bleda">J. Castro-Bleda</name><name sortKey="Espa A Boquera, S" sort="Espa A Boquera, S" uniqKey="Espa A Boquera S" first="S." last="Espa A-Boquera">S. Espa A-Boquera</name><name sortKey="Espa A Boquera, S" sort="Espa A Boquera, S" uniqKey="Espa A Boquera S" first="S." last="Espa A-Boquera">S. Espa A-Boquera</name><name sortKey="Zamora Martinez, F" sort="Zamora Martinez, F" uniqKey="Zamora Martinez F" first="F." last="Zamora-Martínez">F. Zamora-Martínez</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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