Non-standard inferences for knowledge-based image retrieval
Non-standard inferences for knowledge-based image retrieval
- Author(s): T. Di Noiat ; E. Di Sciascio ; F.M. Donin ; F. di Cugno ; E. Tinelli
- DOI: 10.1049/ic.2005.0731
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- Author(s): T. Di Noiat ; E. Di Sciascio ; F.M. Donin ; F. di Cugno ; E. Tinelli Source: 2nd European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology (EWIMT 2005), 2005 p. 191 – 197
- Conference: 2nd European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology (EWIMT 2005)
- DOI: 10.1049/ic.2005.0731
- ISBN: 0 86341 595 4
- Location: London, UK
- Conference date: 30 Nov.-1 Dec. 2005
- Format: PDF
As more multimedia content is semantically annotated there is the need to fully benefit from the efforts devoted to the annotation. Such benefit is obvious when thinking of standard inference services such as classification and satisfiability, yet we believe there are other, smart, services that can complement basic ones and provide smarter queries on annotated image repositories. In this paper we show and motivate how Concept Abduction and Concept Contraction, recently introduced inference services in Description Logics, can be used for semantic based query and query refinement of annotated images.
Inspec keywords: knowledge based systems; content-based retrieval; inference mechanisms; image retrieval; multimedia computing
Subjects: Computer vision and image processing techniques; Knowledge engineering techniques; Multimedia; Information retrieval techniques
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