Semantics of user interface for image retrieval: Possibility theory and learning techniques
Identifieur interne : 00E879 ( Main/Merge ); précédent : 00E878; suivant : 00E880Semantics of user interface for image retrieval: Possibility theory and learning techniques
Auteurs : M. Créhange [France] ; O. Foucaut [France] ; G. Halin [France] ; N. Mouaddib [France] ; J. F. FoucautSource :
- Information Processing and Management [ 0306-4573 ] ; 1989.
English descriptors
- Teeft :
- Artificial intelligence approach, Boolean, Certain understanding, Characteristic function, Characteristic functions, Computer science, Crin nancy, Data type, Database, Demand concept, Demand terms, Descriptive data base, Different domains, Different kinds, Essential role, Expression level, Expressivity, Expressivity weight, Expressivity weights, Exprim, Exprim system, Feature name, First test, Fitting images, Foucaut, Fuzzy data retrieval, Fuzzy sets, Hazy, Hazy data, Hazy database, Hazy information, Hazy request, Heuristic, Image base, Image database, Image description, Image descriptions, Image retrieval, Imageur documentaire, Information retrieval, Information retrieval systems, Informations nuancees, Interactive, Interactive image retrieval, Interface, Internal report, Intrinsic properties, Method principle, Mushroom, Mycomatic, Negative examples, Paris images, Photo contents, Piloting module, Positive examples, Possibility theory, Precise determination, Production rules, Reformulation, Relevance feedback, Relevance weight, Request reformulation, Retrieval, Rich semantics, Semantic network, Semantic relations, Semantics, Specific features, Specific terms, Such applications, System query, Thesaurus, Thesaurus scanning, User, User interface, Visual inference, Visualization, Visualization phase.
Abstract
Abstract: We explain and justify the need of a rich semantics for the user interface in interactive image retrieval involving new media. Two ideas to build such interfaces are presented: possibility theory applied to fuzzy data retrieval (with hazy data and/or requests) and a machine learning technique applied to learning the user's deep need (with chosen images as examples and rejected ones as negative examples). We present two prototypes we have realized or undertaken using videodisks and knowledge-based softwares.
Url:
DOI: 10.1016/0306-4573(89)90096-4
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Foucaut</name></author></analytic><monogr></monogr><series><title level="j">Information Processing and Management</title><title level="j" type="abbrev">IPM</title><idno type="ISSN">0306-4573</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1989">1989</date><biblScope unit="volume">25</biblScope><biblScope unit="issue">6</biblScope><biblScope unit="page" from="615">615</biblScope><biblScope unit="page" to="627">627</biblScope></imprint><idno type="ISSN">0306-4573</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0306-4573</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="Teeft" xml:lang="en"><term>Artificial intelligence approach</term><term>Boolean</term><term>Certain understanding</term><term>Characteristic function</term><term>Characteristic functions</term><term>Computer science</term><term>Crin nancy</term><term>Data type</term><term>Database</term><term>Demand concept</term><term>Demand terms</term><term>Descriptive data base</term><term>Different domains</term><term>Different kinds</term><term>Essential role</term><term>Expression level</term><term>Expressivity</term><term>Expressivity weight</term><term>Expressivity weights</term><term>Exprim</term><term>Exprim system</term><term>Feature name</term><term>First test</term><term>Fitting images</term><term>Foucaut</term><term>Fuzzy data retrieval</term><term>Fuzzy sets</term><term>Hazy</term><term>Hazy data</term><term>Hazy database</term><term>Hazy information</term><term>Hazy request</term><term>Heuristic</term><term>Image base</term><term>Image database</term><term>Image description</term><term>Image descriptions</term><term>Image retrieval</term><term>Imageur documentaire</term><term>Information retrieval</term><term>Information retrieval systems</term><term>Informations nuancees</term><term>Interactive</term><term>Interactive image retrieval</term><term>Interface</term><term>Internal report</term><term>Intrinsic properties</term><term>Method principle</term><term>Mushroom</term><term>Mycomatic</term><term>Negative examples</term><term>Paris images</term><term>Photo contents</term><term>Piloting module</term><term>Positive examples</term><term>Possibility theory</term><term>Precise determination</term><term>Production rules</term><term>Reformulation</term><term>Relevance feedback</term><term>Relevance weight</term><term>Request reformulation</term><term>Retrieval</term><term>Rich semantics</term><term>Semantic network</term><term>Semantic relations</term><term>Semantics</term><term>Specific features</term><term>Specific terms</term><term>Such applications</term><term>System query</term><term>Thesaurus</term><term>Thesaurus scanning</term><term>User</term><term>User interface</term><term>Visual inference</term><term>Visualization</term><term>Visualization phase</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: We explain and justify the need of a rich semantics for the user interface in interactive image retrieval involving new media. 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