Algorithms for clustering data
Algorithms for clustering data
Partial Shape Recognition Using Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Partial Shape Recognition: A Landmark-Based Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
An Efficiently Computable Metric for Comparing Polygonal Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching general polygonal arcs
CVGIP: Image Understanding
Attributed String Matching by Split-and-Merge for On-Line Chinese Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Image Retrieval by Elastic Matching of User Sketches
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data clustering using a model granular magnet
Neural Computation
Learning Visual Models from Shape Contours Using Multiscale Convex/Concave Structure Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computation of Normalized Edit Distance and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Shape Analysis Model with Applications to a Character Recognition System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modal Matching for Correspondence and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity and Affine Invariant Distances Between 2D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Flexible Syntactic Matching of Curves
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Object Recognition Using Subspace Methods
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
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The organization of image databases can rely upon different aspects of image similarity. Here we extract silhouettes from images of three dimensional objects, and rely upon curve similarity for image classification. Our scheme avoids the embedding of images in a vector space. Instead, we propose a curve dissimilarity measure which relies upon a novel curve matching syntactic algorithm, and use it to represent the database as a complete graph, with nodes representing the images and dissimilarity values assigning weights to the edges. A robust clustering algorithm, which is based on a physical ferromagnet model, is used to find the hierarchical structure underlying the collection of images. We tested our scheme with a database of 90 real images of 6 objects, some of them very different, others rather similar. We get a perfect hierarchical classification of these images into 6 classes of objects belonging to 3 different families.