Image Querying

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.