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Conference papers

Integrating Similarity-based Queries in Image DBMSs

Abstract : Until recently, issues in image retrieval have been handled in DBMSs and in computer vision as separate research works. Nowadays, the trend is towards integrating the two approaches (content- and metadata-based) for multi-criteria image retrieval. However, most existing works and proposals in this domain lack a formal framework to deal with a multi-criteria query. In this paper, we introduce a formal framework to address this subject of image retrieval under an ORDBMS model. We first propose an image data repository model interoperable with current popular standards. Then, we present an algebraic formalism for content-based operators on image database. We study the properties of these operators and discuss query optimization issues. To demonstrate the use of our algebra, we implemented an extension of our prototype called EMIMS. Experimental evaluations on our proposed query optimization techniques used in EMIMS are presented here.
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Submitted on : Wednesday, April 26, 2017 - 3:35:39 PM
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Solomon Atnafu, Richard Chbeir, David Coquil, Lionel Brunie. Integrating Similarity-based Queries in Image DBMSs. ACM Symposium on Applied Computing (SAC'04), Mar 2004, Nicosia, Cyprus. pp.735-739, ⟨10.1145/967900.968052⟩. ⟨hal-01514712⟩



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