A texture thesaurus for browsing large aerial photographs
Journal of the American Society for Information Science - Special topic issue: artificial intelligence techniques for emerging information systems applications
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object-based queries using color points of interest
CBAIVL '01 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'01)
Unsupervised Robust Clustering for Image Database Categorization
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mental image search by boolean composition of region categories
Multimedia Tools and Applications
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The Query By Visual Thesaurus (QBVT) paradigm has strongly contributed to the visual information retrieval objective when no starting example is available. The Visual Thesaurus is a representative summary of all the visual patches in the database. Its reliable construction helps the user expression a ”mental image” by composing the visual patches according to the details he has in mind. In this paper, we introduce a relational clustering algorithm (CARD) to build the Visual Thesaurus from regions finely described by variable signature dimensions. The resulting visual categories depict the variability of regions based on local color points of interest. Therefore, we extend first the notion of image matching to regions using non-traditional metrics suitable for the multi-dimensional variables. We also, introduce an appropriate relational clustering for regions categorization using the similarity matrix induced by the latter metrics. Moreover, we propose an efficient method to speed up distance computation and reduce the feature representatives based on adaptive clustering. Our approach was tested on generic images and gives perceptually relevant visual categories.