The query by image content (QBIC) system
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Survey on Content-Based Retrieval for Multimedia Databases
IEEE Transactions on Knowledge and Data Engineering
Learning and Feature Selection in Stereo Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extending Relational atabases to Support Content-based Retrieval of Medical Images
CBMS '02 Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems (CBMS'02)
Integrating similarity-based queries in image DBMSs
Proceedings of the 2004 ACM symposium on Applied computing
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This paper proposes a formal representation of the operations required to perform content-based image retrieval (CBIR) in large relational databases, using similarity queries. In this paper, we consider similarity as a numerical value obtained comparing a pair of images, which is calculated by a distance (dissimilarity) function. Distance functions usually rely on a set of features extracted from each image through a set of image processing algorithms called feature extractors. Before extracting features, other image processing algorithms are usually employed to pre-process each image, preparing it for the extractors. Usually there are several criteria that can be considered when measuring how much two images are similar. Therefore, to compare images in current CBIR environments one must define (1) the criteria, (2) the image pre-processing needed before the extractors can be executed, (3) which are those extractors, (4) which features must be considered, (5) and which distance function must be used. All of these definitions must have been set before a comparison can be performed. The complexity of defining how to compare images has lead to the development of systems aiming CBIR that allow relatively few options to configure the image comparison operations. Moreover, no formal representation of the entire CBIR process exists. In this paper we present such a formal environment, where all above-mentioned definitions are represented, entailing the development of flexible and highly-configurable CBIR systems. We also report a system developed using this formalism that enables the content-based retrieval of medical images from a hospital database, thus showing results of applying the presented formalism in a real environment.