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Stochastic Complexity in Statistical Inquiry Theory
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Multiple Regions and Their Spatial Relationship-Based Image Retrieval
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Max-margin Multiple-Instance Learning via Semidefinite Programming
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Hidden semantic concept discovery in region based image retrieval
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Bag dissimilarities for multiple instance learning
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Image clustering using multimodal keywords
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
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MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
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Colour texture segmentation using modelling approach
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ORF-NT: an object-based image retrieval framework using neighborhood trees
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
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AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
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Practical region-based matching for stereo vision
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
Region filtering using color and texture features for image retrieval
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CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Automated object extraction for medical image retrieval using the insight toolkit (ITK)
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Correlation learning method based on image internal semantic model for CBIR
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Content based image retrieval using region labelling
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An Image Clustering and Feedback-based Retrieval Framework
International Journal of Multimedia Data Engineering & Management
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Blobworld is a system for image retrieval based on finding coherent image regions which roughly correspond to objects. Each image is automatically segmented into regions ("blobs") with associated color and texture descriptors. Queryingi s based on the attributes of one or two regions of interest, rather than a description of the entire image. In order to make large-scale retrieval feasible, we index the blob descriptions usinga tree. Because indexing in the high-dimensional feature space is computationally prohibitive, we use a lower-rank approximation to the high-dimensional distance. Experiments show encouraging results for both queryinga nd indexing.