Generic Neighborhood Operators
IEEE Transactions on Pattern Analysis and Machine Intelligence
An active vision architecture based on iconic representations
Artificial Intelligence - Special volume on computer vision
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Recognizing Surfaces Using Three-Dimensional Textons
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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In this article we learn significant local appearance features for visual classes. Generic feature detectors are obtained by unsupervised learning using clustering. The resulting clusters, referred to as "classtons", identify the significant class characteristics from a small set of sample images. The classton channels mark these characteristics reliably using a probabilistic cluster representation. The classtons demonstrate good generalisation with respect to viewpoint changes and previously unseen objects. In all experiments, the classton channels of similar images have the same spatial relations. Learning of these relations allows to generate a classification model that combines the generalisation ability from the classtons and the discriminative power from the spatial relations.