Semantic based image retrieval: a probabilistic approach
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: A System for Region-Based Image Indexing and Retrieval
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
MPEG-7 Color Descriptors and Their Applications
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Image Database Retrieval with Multiple-Instance Learning Techniques
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
OCRS: an Interactive Object-based Image Clustering and Retrieval System
MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
Region-based image clustering and retrieval using multiple instance learning
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
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Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region object can hardly represent the user's retrieval target, especially when more than one object of interest is involved in the retrieval. Integrated Region Matching IRM has been used to improve the retrieval accuracy by evaluating the overall similarity between images and incorporating the properties of all the regions in the images. However, IRM does not take the user's preferred regions into account and has undesirable time complexity. In this article, we present a Feedback-based Image Clustering and Retrieval Framework FIRM using a novel image clustering algorithm and integrating it with Integrated Region Matching IRM and Relevance Feedback RF. The performance of the system is evaluated on a large image database, demonstrating the effectiveness of our framework in catching users' retrieval interests in object-based image retrieval.