Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
ACM Computing Surveys (CSUR)
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conceptual Spaces: The Geometry of Thought
Conceptual Spaces: The Geometry of Thought
Finding the flow in web site search
Communications of the ACM
Integrated Browsing and Querying for Image Databases
IEEE MultiMedia
Dynamic Taxonomies: A Model for Large Information Bases
IEEE Transactions on Knowledge and Data Engineering
Ontology-Based Photo Annotation
IEEE Intelligent Systems
Faceted metadata for image search and browsing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Search strategies in content-based image retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Uniform Access to Multimedia Information Bases through Dynamic Taxonomies
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
The Intelligent e-Store: Easy Interactive Product Selection and Comparison
CEC '05 Proceedings of the Seventh IEEE International Conference on E-Commerce Technology
Content-based image retrieval: approaches and trends of the new age
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Analysis and validation of information access through mono, multidimensional and dynamic taxonomies
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
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Retrieval in image information bases has been traditionally addressed by two different and unreconciled approaches: the first one uses normal query methods on metadata or on a textual description of each item. The second one works on low-level multimedia features (such as color, texture, etc.) and tries to find items that are similar to a specific selected item. Neither of these approaches supports the most common end-user task: the exploration of an information base in order to find the "right" items. This paper describes a prototype system based on dynamic taxonomies, a model for the intelligent exploration of heterogeneous information bases, and shows how the system implements a new access paradigm supporting guided exploration, discovery, and the seamless integration of access through metadata with methods based on low-level multimedia features. Example interactions are discussed, as well as the major implications of this approach.