Iconic indexing by 2-D strings
IEEE Transactions on Pattern Analysis and Machine Intelligence
A topological data model for spatial databases
SSD '90 Proceedings of the first symposium on Design and implementation of large spatial databases
CORE: a content-based retrieval engine for multimedia information systems
Multimedia Systems - Special issue on content-based retrieval
Fast parallel similarity search in multimedia databases
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Managing multimedia information in database systems
Communications of the ACM
A robust framework for content-based retrieval by spatial similarity in image databases
ACM Transactions on Information Systems (TOIS)
Medical Image Analysis: Progress over Two Decades and the Challenges Ahead
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimedia Metadata Management Handbook: Integrating and Applying Digital Data
Multimedia Metadata Management Handbook: Integrating and Applying Digital Data
Knowledge-Based Image Retrieval with Spatial and Temporal Constructs
IEEE Transactions on Knowledge and Data Engineering
A Survey on Content-Based Retrieval for Multimedia Databases
IEEE Transactions on Knowledge and Data Engineering
Crossing the Divide Between Computer Vision and Databases in Search of Image Databases
VDB4 Proceedings of the IFIP TC2/WG 2.6 Fourth Working Conference on Visual Database Systems 4
EMIR2: An Extended Model for Image Representation and Retrieval
DEXA '95 Proceedings of the 6th International Conference on Database and Expert Systems Applications
A Global Description of Medical Image with High Precision
BIBE '00 Proceedings of the 1st IEEE International Symposium on Bioinformatics and Biomedical Engineering
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Since the last decade, images have been integrated into several application domains such as GIS, medicine, etc. This integration necessitates new managing methods particularly in image retrieval. Queries should be formulated using different types of features such as low-level features of images (histograms, color distribution, etc.), spatial and temporal relations between salient objects, semantic features, etc. In this chapter, we propose a novel method for identifying and indexing several types of relations between salient objects. Spatial relations are used here to show how our method can provide high expressive power to relations in comparison to the traditional methods.