Tool wear status recognition based on Mahalanobis distance

Character Recognition with Mahalanobis Distance Based on Between-cluster Information

In the case of using the Mahalanobis distance as discriminant function, usually the covariance matrix calculated from training samples is used. However, it is extremely difficult to prepare enough training samples if the dimension of feature vector is large. Therefore, estimated eigenvalues and eigenvectors of covariance matrix will include errors that cause misclassification. In this paper, a ...

Normalized Clustering Algorithm Based on Mahalanobis Distance

FCM (fuzzy c-means algorithm) based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. The added fuzzy covariance matrices in their distance measure were not directly derived from the objective function. In this paper, an improved Normalized Clustering Algorithm Based on Mahalanobis distance by t...

Anisotropic Superpixel Generation Based on Mahalanobis Distance

Superpixels have been widely used as a preprocessing step in various computer vision tasks. Spatial compactness and color homogeneity are the two key factors determining the quality of the superpixel representation. In this paper, these two objectives are considered separately and anisotropic superpixels are generated to better adapt to local image content. We develop a unimodular Gaussian gene...

Mahalanobis distance based on fuzzy clustering algorithm for image segmentation

A cardinal dissensus measure based on the Mahalanobis distance

In this paper we address the problem of measuring the degree of consensus/dissensus in a context where experts or agents express their opinions on alternatives or issues by means of cardinal evaluations. To this end we propose a new class of distance-based consensus model, the family of the Mahalanobis dissensus measures for profiles of cardinal values. We set forth some meaningful properties o...