Algorithms for clustering data
Algorithms for clustering data
Active shape models—their training and application
Computer Vision and Image Understanding
International Journal of Computer Vision
FORMS: a flexible object recognition and modeling system
International Journal of Computer Vision
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification with Nonmetric Distances: Image Retrieval and Class Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
A shock grammar for recognition
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Analysis of Planar Shapes Using Geodesic Paths on Shape Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Axis-Based Representation for Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Grouping with Asymmetric Affinities: A Game-Theoretic Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering Shape Classes using Tree Edit-Distance and Pairwise Clustering
International Journal of Computer Vision
Dissimilarity between two skeletal trees in a context
Pattern Recognition
Disconnected Skeleton: Shape at Its Absolute Scale
IEEE Transactions on Pattern Analysis and Machine Intelligence
Foreground Focus: Unsupervised Learning from Partially Matching Images
International Journal of Computer Vision
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The search of a model for representing and evaluating the similarities between shapes in a perceptually coherent way is still an open issue. One reason for this is that our perception of similarities is strongly influenced by the underlying category structure. In this paper we aim at jointly learning the categories from examples and the similarity measures related to them. There is a chicken and egg dilemma here: class knowledge is required to determine perceived similarities, while the similarities are needed to extract class knowledge in an unsupervised way. The problem is addressed through a game theoretic approach which allows us to compute 2D shape categories based on a skeletal representation. The approach provides us with both the cluster information needed to extract the categories, and the relevance information needed to compute the category model and, thus, the similarities. Experiments on a database of 1000 shapes showed that the approach outperform other clustering approaches that do not make use of the underlying contextual information and provides similarities comparable with a state-of-the-art label-propagation approach which, however, cannot extract categories.