This paper reviews and extends the field of similarity-based classification, presenting new
analyses, algorithms, data sets, and a comprehensive set of experimental results for a rich …

In this paper, we propose a tracking algorithm based on a multi-feature joint sparse
representation. The templates for the sparse representation can include pixel values …

Recently hybrid generative discriminative approaches have emerged as an efficient
knowledge representation and data classification engine. However, little attention has been …

The text skew estimation algorithm utilizes recursive morphological transforms. With hand
tuned parameters the algorithm produces estimated text skew angles which are within …

Traditional nearest neighbor classifiers based on usual distance functions (eg, Euclidean
distance) often suffer in high dimension low sample size (HDLSS) situations, where …

Moving target tracking from the infrared images is a challenging task due to the targets'
unstable appearance, complex background clutters and a limited number of pixels for each …

We present a new technique that employs support vector machines (SVMs) and Gaussian
mixture densities (GMDs) to create a generative/discriminative object classification …

For data with more variables than the sample size, phenomena like concentration of
pairwise distances, violation of cluster assumptions and presence of hubness often have …

The Brunswik lens model typically represents a judge's accuracy using parameters derived
from linear regression. This is not optimal if the judgment or the ecological criterion is …

In this paper we present Similarity Neural Networks (SNNs), a neural network model able to
learn a similarity measure for pairs of patterns, exploiting a binary supervision on their …