Photobook: content-based manipulation of image databases
International Journal of Computer Vision
Artificial Intelligence Review - Special issue on lazy learning
Visual information retrieval from large distributed online repositories
Communications of the ACM
IMKA: a multimedia organization system combining perceptual and semantic knowledge
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Visually Searching the Web for Content
IEEE MultiMedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
ImageRover: A Content-Based Image Browser for the World Wide Web
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
Configuration based scene classification and image indexing
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Supporting Content-based Queries over Images in MARS
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
Semantic Organization of Scenes Using Discriminant Structural Templates
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Color-spatial image indexing and applications
Color-spatial image indexing and applications
Spatial Color Indexing and Applications
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
The PicToSeek WWW Image Search System
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
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Organizing images into semantic categories can be extremely useful for content-based image retrieval and image annotation. Grouping images into semantic classes is a difficult problem, however. Image classification attempts to solve this hard problem by using low-level image features. In this paper, we propose amethod for hierarchical classification of images via supervised learning. This scheme relies on using a good low-level feature and subsequently performing feature-space reconfiguration using singular value decomposition to reduce noise and dimensionality. We use the training data to obtain a hierarchical classification tree that can be used to categorize new images. Our experimental results suggest that this scheme not only performs better than standard nearest-neighbor techniques, but also has both storage and computational advantages.