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Multi-feature query language for image classification

Pein, Raoul Pascal and Lu, Joan (2010) Multi-feature query language for image classification. Procedia Computer Science, 1 (1). pp. 2533-2541. ISSN 1877-0509

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    Abstract

    Despite the major effort put into the creation of Content-Based Image Retrieval (CBIR) system
    during the last decade, the solutions available are still not satisfying for generic purposes.
    The most severe issue seems to be the so-called “semantic gap”. It is feasible to define and use
    domain specific feature vectors on a low level and use this information for a similarity based retrieval.
    Yet, mapping these to higher level semantics remains dicult. This research investigates
    a domain-independent way of automatized image categorization. A CBIR query language is constructed
    to build query-like descriptors for each category to be learned. The proposed learning
    algorithm is based on decision-trees. The resulting descriptors are aimed to be understandable
    and modifiable by expert users. A case-study is presented to support these claims.

    Item Type: Article
    Additional Information: Paper presented at the International Conference on Computational Science (iccs) 2010 University of Amsterdam The Netherlands May 31 - June 2, 2010
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Schools: School of Computing and Engineering
    School of Computing and Engineering > Diagnostic Engineering Research Centre
    School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
    School of Computing and Engineering > Informatics Research Group
    School of Computing and Engineering > Informatics Research Group > Software Engineering Research Group
    School of Computing and Engineering > Informatics Research Group > XML, Database and Information Retrieval Research Group
    Related URLs:
    Depositing User: Raoul Pein
    Date Deposited: 25 May 2010 11:38
    Last Modified: 09 Dec 2010 12:32
    URI: http://eprints.hud.ac.uk/id/eprint/7671

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