Shape matching and object recognition plays an vital role in the computer vision. The shape matching is difficult in case of the real world images like mpeg database images since the real world images has the internal and external contours. The Mahalanobis distance based shape context approach is proposed to measure similarity between shapes and exploit it for shape retrieval. The process of sh...

We introduce a novel variational method that allows to approximately integrate out kernel hyperparameters, such as length-scales, in Gaussian process regression. This approach consists of a novel variant of the variational framework that has been recently developed for the Gaussian process latent variable model which additionally makes use of a standardised representation of the Gaussian proces...

Statistical distance measures have found wide applicability in information retrieval tasks that typically involve high dimensional datasets. In order to reduce the storage space and ensure efficient performance of queries, dimensionality reduction while preserving the inter-point similarity is highly desirable. In this paper, we investigate various statistical distance measures from the point o...

We address the problem of learning distance metrics using side-information in the form of groups of "similar" points. We propose to use the RCA algorithm, which is a simple and efficient algorithm for learning a full ranked Mahalanobis metric (Shental et al., 2002). We first show that RCA obtains the solution to an interesting optimization problem, founded on an information theoretic basis. If ...

Representational similarity analysis of activation patterns has become an increasingly important tool for studying brain representations. The dissimilarity between two patterns is commonly quantified by the correlation distance or the accuracy of a linear classifier. However, there are many different ways to measure pattern dissimilarity and little is known about their relative reliability. Her...

This paper concerns a comparative study on long term text-independent speaker identification using statistical features. Performances of six statistical methods are compared. Four of them are the distance measures (the City block, the Euclidean, the Weighted Euclidean, and the Mahalanobis distance measures). The other two are the Gaussian probability density estimation and the probability estim...

We suggest an extension of the Neural Gas vector quantization method to local principal component analysis. The distance measure for the competition between local units combines a normalized Mahalanobis distance in the principal subspace and the squared reconstruction error, with the weighting of both measures depending on the residual variance in the minor subspace. A recursive least squares m...