Methods for controlling the adaptation process of an on-line handwritten character recognizer are studied. The classifier is based on the k-nearest neighbor rule and it is adapted to a new writing style by adding new prototypes, inactivating confusing prototypes, and reshaping existing prototypes in a self-supervised fashion. The dissimilarity measure used for the comparison of characters is a ...

Based on a local approximation of the Riemannian distance on a manifold by a computationally cheap dissimilarity measure, a time discrete geodesic calculus is developed, and applications to shape space are explored. The dissimilarity measure is derived from a deformation energy whose Hessian reproduces the underlying Riemannian metric, and it is used to define length and energy of discrete path...

This paper presents a new approach to agglomerative hierarchical clustering. Classical hierarchical clustering algorithms are based on metrics which only consider the absolute distance between two clusters, merging the pair of clusters with highest absolute similarity. We propose a relative dissimilarity measure, which considers not only the distance between a pair of clusters, but also how dis...

Originally developed in bioinformatics, sequence analysis is being increasingly used in social sciences for the study of life-course processes. The methodology generally employed consists in computing dissimilarities between the trajectories and, if typologies are sought, in clustering the trajectories according to their similarities or dissemblances. The choice of an appropriate dissimilarity ...

The ordering of words in category fluency lists is indicative of the semantic distance between items in conceptual memory. Several studies have concluded from structural analyses of such data, using cluster analysis or multidimensional scaling, that the semantic memory of patients with schizophrenia is more disorganized than that of controls. Previous studies have based their analyses on a meas...

The K-Means clustering is by far the most widely used method for discovering clusters in data. It has a good performance on the data with compact super-sphere distributions, but tends to fail in the data organized in more complex and unknown shapes. In this paper, we analyze in detail the characteristic property of data clustering and propose a novel dissimilarity measure, named density-sensiti...

This paper presents a novel variational model for inverse consistent deformable image registration. This model deforms the source and target image simultaneously, and aligns the deformed source and deformed target images in the way that the both transformations are inverse consistent. The model does not computes the inverse transforms explicitly, alternatively it finds two more deformation fiel...

Re-identifying the same person in different images is a distinct challenge for visual surveillance systems. Building an accurate correspondence between highly variable images requires a suitable dissimilarity measure. To date, most existing measures have used adapted distance based on a learned metric. Unfortunately, real-world human image data, which tends to show large intra-class variations ...

We introduce a novel rich local descriptor of an image point, we call the (labeled) distance set, which is determined by the spatial arrangement of image features around that point. We describe a two-dimensional (2D) visual object by the set of (labeled) distance sets associated with the feature points of that object. Based on a dissimilarity measure between (labeled) distance sets and a dissim...