This paper proposes an adaptive Mahalanobis distance for face retrieval. The distance is derived from a posterior distribution of observation errors in features categorized by con dence of face images. Since the distance is calculated considering error variances of each dimension according to the con dence, it can re ect error distribution of each matching more precisely than a standard Mahalan...

A distance for mixed nominal, ordinal and continuous data is developed by applying the Kullback–Leibler divergence to the general mixed-data model, an extension of the general location model that allows for ordinal variables to be incorporated in the model. The distance obtained can be considered as a generalization of the Mahalanobis distance to data with a mixture of nominal, ordinal and cont...

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...

Autonomous robot navigation in outdoor environments remains a challenging and unsolved problem. A key issue is our ability to identify safe or navigable paths far enough ahead of the robot to allow smooth trajectories at acceptable speeds. Colour or texture-based labeling of safe path regions in image sequences is one way to achieve this far field prediction. A challenge for classifiers identif...

Most distance metric learning (DML) approaches focus on learning a Mahalanobis metric for measuring distances between examples. However, for particular feature representations, e.g., histogram features like BOW and SPM, Mahalanobis metric could not model the correlations between these features well. In this work, we define a nonMahalanobis distance for histogram features, via Expected Hitting T...

The scaling parameter α helps maintain a balance between supervised and unsupervised learning in semi-supervised Fuzzy c-Means (ssFCM). In this study, we investigated the effects of different α values, 0.1, 0.5, 1 and 10 in Pedrycz and Waletsky’s ssFCM with various amounts of labelled data, 10%, 20%, 30%, 40%, 50% and 60% and three distance metrics, Euclidean, Mahalanobis and kernel-based on th...