Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
The nature of statistical learning theory
The nature of statistical learning theory
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrated Browsing and Querying for Image Databases
IEEE MultiMedia
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
FeedbackBypass: A New Approach to Interactive Similarity Query Processing
Proceedings of the 27th International Conference on Very Large Data Bases
iSearch: Mining Retrieval History for Content-Based Image Retrieval
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
An efficient memorization scheme for relevance feedback in image retrieval
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Imagination: Exploiting Link Analysis for Accurate Image Annotation
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
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In this paper an effective context-based approach for interactive similarity queries is presented. By exploiting the notion of image “context”, it is possible to associate different meanings to the same query image. This is indeed necessary to model complex query concepts that, due to their nature, cannot be effectively represented without contextualize the target image. The context model is simple yet effective and consists of a set of significant images (possibly not relevant to the query) that describe the semantic meaning the user is interested in. When feedback is present, the query context assumes a dynamic nature, changing over time depending on the actual retrieved images judged as relevant by the user for her current search task. Moreover, the proposed approach is able to complement the role of relevance feedback by persistently maintaining the query parameters determined through user interaction over time and ensuring search efficiency. Experimental results on a database of about 10,000 images show the high quality contribution of the proposed approach.