Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
The nature of statistical learning theory
The nature of statistical learning theory
A case for interaction: a study of interactive information retrieval behavior and effectiveness
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
RELIEF: combining expressiveness and rapidity into a single system
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A unified framework for semantics and feature based relevance feedback in image retrieval systems
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Explicit query formulation with visual keywords
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Symbolic photograph content-based retrieval
Proceedings of the eleventh international conference on Information and knowledge management
Unifying Keywords and Visual Contents in Image Retrieval
IEEE MultiMedia
Blobworld: A System for Region-Based Image Indexing and Retrieval
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
EMIR2: An Extended Model for Image Representation and Retrieval
DEXA '95 Proceedings of the 6th International Conference on Database and Expert Systems Applications
Combining Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
IEEE Transactions on Image Processing
Semantic queries in databases: problems and challenges
Proceedings of the 18th ACM conference on Information and knowledge management
Proceedings of the 16th International Conference on Extending Database Technology
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Performance of state-of-the-art image retrieval systems is strongly limited due to the difficulty of accurately relating semantics conveyed by images to low-level extracted features. Moreover, dealing with the problem of combining modalities for querying is of huge importance in forthcoming retrieval methodologies and is the only solution for achieving significant retrieval performance on image documents. This paper presents an architecture addressing both of these issues which is based on an expressive formalism handling high-level image descriptions. First, it features a multi-facetted conceptual framework which integrates semantics and signal characterizations and operates on image objects (abstractions of visual entities within a physical image) in an attempt to perform indexing and querying operations beyond trivial low-level processes and region-based frameworks. Then, it features a query-by-example framework based on high-level image descriptions instead of their extracted low-level features and operate both on semantics and signal features. The flexibility of this module and the rich query language it offers, consisting of both boolean and quantification operators, lead to optimized user interaction and increased retrieval performance. Experimental results on a test collection of 2500 images show that our approach gives better results in terms of recall and precision measures than state-of-the-art frameworks which couple loosely keyword-based query modules and relevance feedback processes operating on low-level features.