Topic
Mahalanobis distance
About: Mahalanobis distance is a research topic. Over the lifetime, 4616 publications have been published within this topic receiving 95294 citations.
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TL;DR: In this paper, the authors compare the reliability of three classes of dissimilarity measures: classification accuracy, Euclidean/Mahalanobis distance, and Pearson correlation distance, using simulations and four real functional magnetic resonance imaging (fMRI) datasets.
416 citations
TL;DR: In this article, the authors compared several approaches for imputing the response variables BA and TD, aggregated at the plot-scale and species-level, from topographic and canopy structure predictor variables derived from discrete-return airborne LiDAR data.
401 citations
TL;DR: It is shown that learning the pooling regions for the descriptor can be formulated as a convex optimisation problem selecting the regions using sparsity, and an extension of the learning formulations to a weakly supervised case, which allows us to learn the descriptors from unannotated image collections.
Abstract: The objective of this work is to learn descriptors suitable for the sparse feature detectors used in viewpoint invariant matching. We make a number of novel contributions towards this goal. First, it is shown that learning the pooling regions for the descriptor can be formulated as a convex optimisation problem selecting the regions using sparsity. Second, it is shown that descriptor dimensionality reduction can also be formulated as a convex optimisation problem, using Mahalanobis matrix nuclear norm regularisation. Both formulations are based on discriminative large margin learning constraints. As the third contribution, we evaluate the performance of the compressed descriptors, obtained from the learnt real-valued descriptors by binarisation. Finally, we propose an extension of our learning formulations to a weakly supervised case, which allows us to learn the descriptors from unannotated image collections. It is demonstrated that the new learning methods improve over the state of the art in descriptor learning on the annotated local patches data set of Brown et al. and unannotated photo collections of Philbin et al. .
400 citations
TL;DR: It is shown that the many methods and variants that have been applied to pollen data can nevertheless be understood within a single conceptual frame, and it is suggested that MDS may sometimes be more informative with wide-ranging data sets.
392 citations
TL;DR: This article describes statistical data association techniques originally developed in the context of target tracking and surveillance and now beginning to be used in dynamic motion analysis by the computer vision community.
Abstract: Motion correspondence is a fundamental problem in computer vision and many other disciplines. This article describes statistical data association techniques originally developed in the context of target tracking and surveillance and now beginning to be used in dynamic motion analysis by the computer vision community. The Mahalanobis distance measure is first introduced before discussing the limitations of nearest neighbor algorithms. Then, the track-splitting, joint likelihood, multiple hypothesis algorithms are described, each method solving an increasingly more complicated optimization. Real-time constraints may prohibit the application of these optimal methods. The suboptimal joint probabilistic data association algorithm is therefore described. The advantages, limitations, and relationships between the approaches are discussed.
388 citations