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 article, the standard distance for the quantity in univariate analysis is generalized to the multivariate situation, where it coincides with the square root of the Mahalanobis distance between two samples.
Abstract: We propose to use the term standard distance for the quantity in univariate analysis and show that it can be easily generalized to the multivariate situation, where it coincides with the square root of the Mahalanobis distance between two samples.
245 citations
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TL;DR: In this article, a quantitative comparison of the similarity measures in function of varying time series and ecosystem characteristics, such as amplitude, timing, and noise effects, is presented, and the performance of the commonly used similarity measures (D), ranging from Manhattan (DMan), Euclidean (DE) and Mahalanobis (DMah) distance measures, to correlation (DCC), Principal Component Analysis (PCA; DPCA) and Fourier based (DFFT,Dξ,DFk) similarities.
243 citations
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TL;DR: This paper reformulates person reidentification in a camera network as a multitask distance metric learning problem, and presents a novel multitask maximally collapsing metric learning (MtMCML) model that works substantially better than other current state-of-the-art person reIdentification methods.
Abstract: Person reidentification in a camera network is a valuable yet challenging problem to solve. Existing methods learn a common Mahalanobis distance metric by using the data collected from different cameras and then exploit the learned metric for identifying people in the images. However, the cameras in a camera network have different settings and the recorded images are seriously affected by variability in illumination conditions, camera viewing angles, and background clutter. Using a common metric to conduct person reidentification tasks on different camera pairs overlooks the differences in camera settings; however, it is very time-consuming to label people manually in images from surveillance videos. For example, in most existing person reidentification data sets, only one image of a person is collected from each of only two cameras; therefore, directly learning a unique Mahalanobis distance metric for each camera pair is susceptible to over-fitting by using insufficiently labeled data. In this paper, we reformulate person reidentification in a camera network as a multitask distance metric learning problem. The proposed method designs multiple Mahalanobis distance metrics to cope with the complicated conditions that exist in typical camera networks. We address the fact that these Mahalanobis distance metrics are different but related, and learned by adding joint regularization to alleviate over-fitting. Furthermore, by extending, we present a novel multitask maximally collapsing metric learning (MtMCML) model for person reidentification in a camera network. Experimental results demonstrate that formulating person reidentification over camera networks as multitask distance metric learning problem can improve performance, and our proposed MtMCML works substantially better than other current state-of-the-art person reidentification methods.
243 citations
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07 Jun 2015TL;DR: This paper presents an explicit polynomial kernel feature map, which is capable of characterizing the similarity information of all pairs of patches between two images, called soft-patch-matching, instead of greedily keeping only the best matched patch, and thus more robust.
Abstract: In this paper, we address the person re-identification problem, discovering the correct matches for a probe person image from a set of gallery person images. We follow the learning-to-rank methodology and learn a similarity function to maximize the difference between the similarity scores of matched and unmatched images for a same person. We introduce at least three contributions to person re-identification. First, we present an explicit polynomial kernel feature map, which is capable of characterizing the similarity information of all pairs of patches between two images, called soft-patch-matching, instead of greedily keeping only the best matched patch, and thus more robust. Second, we introduce a mixture of linear similarity functions that is able to discover different soft-patch-matching patterns. Last, we introduce a negative semi-definite regularization over a subset of the weights in the similarity function, which is motivated by the connection between explicit polynomial kernel feature map and the Mahalanobis distance, as well as the sparsity constraint over the parameters to avoid over-fitting. Experimental results over three public benchmarks demonstrate the superiority of our approach.
236 citations
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TL;DR: Experiments show that focusing on a particular band with high discriminatory power improves the detection performance as well as increases the computational efficiency.
233 citations