Image querying refers to the problem of finding objects that are relevant to a user query within image databases. The classical solutions to deal with such problem include the semantic-based approach, for which an image is represented through metadata (e.g., keywords), and the content-based solution, commonly called content-based image retrieval (CBIR), where the image content is represented by means of low-level features (e.g., color and texture). While for the semantic-based approach the image querying problem is transformed into an information retrieval problem, for CBIR more sophisticated query evaluation techniques are required, as follows. By means of a graphical user interface (GUI), the user provides a query image, by sketching it using graphical tools, by uploading an image she/he has, or by selecting an image supplied by the system. Low-level features are extracted for such image; such features are then used by the query processor to retrieve the DB images having similar characteristics. How the set of relevant DB images is determined depends on which low-level features are used to characterize image content, on the criterion used to compare image features, on how DB objects are ranked with respect to the query (based on either a quantitative measure of similarity or qualitative preferences), and, finally, on whether the user is interested in the whole query image or only in a part of it. All these aspects strongly influence the query evaluation process.

Image Querying

BARTOLINI, ILARIA
2009

Abstract

Image querying refers to the problem of finding objects that are relevant to a user query within image databases. The classical solutions to deal with such problem include the semantic-based approach, for which an image is represented through metadata (e.g., keywords), and the content-based solution, commonly called content-based image retrieval (CBIR), where the image content is represented by means of low-level features (e.g., color and texture). While for the semantic-based approach the image querying problem is transformed into an information retrieval problem, for CBIR more sophisticated query evaluation techniques are required, as follows. By means of a graphical user interface (GUI), the user provides a query image, by sketching it using graphical tools, by uploading an image she/he has, or by selecting an image supplied by the system. Low-level features are extracted for such image; such features are then used by the query processor to retrieve the DB images having similar characteristics. How the set of relevant DB images is determined depends on which low-level features are used to characterize image content, on the criterion used to compare image features, on how DB objects are ranked with respect to the query (based on either a quantitative measure of similarity or qualitative preferences), and, finally, on whether the user is interested in the whole query image or only in a part of it. All these aspects strongly influence the query evaluation process.
Encyclopedia of Database Systems
1368
1374
I. Bartolini
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/80093
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