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The aim of metric learning is to learn a mapping relationship, which reduces the intraclass distance and increases the interclass distance. As there are a large number of parameters that need to be optimized in traditional metric learning algorithms, they may suffer from high computational complexity and overfitting problems in the case of insufficient training data. To alleviate this, we propose a weakly supervised compositional metric learning (WSCML) method, which utilizes a set of predetermined local discriminant metrics to learn the optimal weight combination of the component metrics. Under the large margin framework, our WSCML can effectively improve the verification accuracy by constraining the Mahalanobis distance of positive sample pairs to be less than a small threshold and that of negative sample pairs to be greater than a large threshold. In addition, we employ three regularization terms to optimize the proposed WSCML, respectively, to control the sparsity of the solution while maintaining its feasibility. Experimental results on KinFaceW-I, fine-grained face verification (FGFV), and Labled Faces in the Wild (LFW) datasets show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Obtaining a continuous relationship between input and output is essential during sensor characteristic determination. However, it is a little challenging for complicated input data, for example, image data. In this article, a sensor characteristic determining method using deep learning Compact yet Efficient Cascade Context-based Estimation (C3E) network, which can establish the continuous relationship between the input and the output, was proposed. A UV sensing system based on this C3E method was presented. The sensor after algorithm optimization has a sensitivity of $1~\mu \text{W}$ /cm2. The limit of detection is $9~\mu \text{W}$ /cm2. The network test time is close to 3 s, and the error rate is 5%, which is very suitable to transplant to the embedded platform and provides solutions for compact instruments. [ABSTRACT FROM AUTHOR]

Harmonic state space (HSS) is seen as an effective impedance modeling method to precisely characterize the internal harmonic features of the modular multilevel converter (MMC). However, the existing MMC impedance models assume the ideal grid, ignoring the grid impedance, and they also do not incorporate the widely used dual-loop control and phase-locked loop (PLL). In this article, a complete MMC impedance model based on HSS is proposed to reveal the grid impedance coupling effect of MMC. The model analysis results demonstrate that the MMC impedance is coupled with the grid impedance due to the internal harmonics. This coupling causes MMC to be affected by the grid impedance and may cause instability. On the other hand, the proposed model not only consists of the ac current and the circulating current control, but also incorporates dc voltage outer loop and PLL with a clear physical meaning. Based on the proposed model, this article illustrates the factors that will enhance the coupling, which shows that the proposed model has to be used to improve the accuracy of the analysis. Finally, effectiveness of the proposed model is verified by simulation and experimental results. [ABSTRACT FROM AUTHOR]

This paper presents a sharable and individual multi-view metric learning (MvML) approach for visual recognition. Unlike conventional metric leaning methods which learn a distance metric on either a single type of feature representation or a concatenated representation of multiple types of features, the proposed MvML jointly learns an optimal combination of multiple distance metrics on multi-view representations, where not only it learns an individual distance metric for each view to retain its specific property but also a shared representation for different views in a unified latent subspace to preserve the common properties. The objective function of the MvML is formulated in the large margin learning framework via pairwise constraints, under which the distance of each similar pair is smaller than that of each dissimilar pair by a margin. Moreover, to exploit the nonlinear structure of data points, we extend MvML to a sharable and individual multi-view deep metric learning (MvDML) method by utilizing the neural network architecture to seek multiple nonlinear transformations. Experimental results on face verification, kinship verification, and person re-identification show the effectiveness of the proposed sharable and individual multi-view metric learning methods. [ABSTRACT FROM AUTHOR]

Metric learning has attracted wide attention in face and kinship verification, and a number of such algorithms have been presented over the past few years. However, most existing metric learning methods learn only one Mahalanobis distance metric from a single feature representation for each face image and cannot make use of multiple feature representations directly. In many face-related tasks, we can easily extract multiple features for a face image to extract more complementary information, and it is desirable to learn distance metrics from these multiple features, so that more discriminative information can be exploited than those learned from individual features. To achieve this, we present a large-margin multi-metric learning (LM3L) method for face and kinship verification, which jointly learns multiple global distance metrics under which the correlations of different feature representations of each sample are maximized, and the distance of each positive pair is less than a low threshold and that of each negative pair is greater than a high threshold. To better exploit the local structures of face images, we also propose a local metric learning and local LM3Lmethods to learn a set of local metrics. Experimental results on three face data sets show that the proposed methods achieve very competitive results compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Metric learning aims to learn a distance function to measure the similarity of samples, which plays an important role in many visual understanding applications. Generally, the optimal similarity functions for different visual understanding tasks are task specific because the distributions for data used in different tasks are usually different. It is generally believed that learning a metric from training data can obtain more encouraging performances than handcrafted metrics [1]-[3], e.g., the Euclidean and cosine distances. A variety of metric learning methods have been proposed in the literature [2]-[5], and many of them have been successfully employed in visual understanding tasks such as face recognition [6], [7], image classification [2], [3], visual search [8], [9], visual tracking [10], [11], person reidentification [12], cross-modal matching [13], image set classification [14], and image-based geolocalization [15]-[17]. [ABSTRACT FROM PUBLISHER]

Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption does not hold in many real visual recognition applications, especially when samples are captured across different data sets. In this paper, we propose a new deep transfer metric learning (DTML) method to learn a set of hierarchical nonlinear transformations for cross-domain visual recognition by transferring discriminative knowledge from the labeled source domain to the unlabeled target domain. Specifically, our DTML learns a deep metric network by maximizing the inter-class variations and minimizing the intra-class variations, and minimizing the distribution divergence between the source domain and the target domain at the top layer of the network. To better exploit the discriminative information from the source domain, we further develop a deeply supervised transfer metric learning (DSTML) method by including an additional objective on DTML, where the output of both the hidden layers and the top layer are optimized jointly. To preserve the local manifold of input data points in the metric space, we present two new methods, DTML with autoencoder regularization and DSTML with autoencoder regularization. Experimental results on face verification, person re-identification, and handwritten digit recognition validate the effectiveness of the proposed methods. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a deep metric learning (DML) approach for robust visual tracking under the particle filter framework. Unlike most existing appearance-based visual trackers, which use hand-crafted similarity metrics, our DML tracker learns a nonlinear distance metric to classify the target object and background regions using a feed-forward neural network architecture. Since there are usually large variations in visual objects caused by varying deformations, illuminations, occlusions, motions, rotations, scales, and cluttered backgrounds, conventional linear similarity metrics cannot work well in such scenarios. To address this, our proposed DML tracker first learns a set of hierarchical nonlinear transformations in the feed-forward neural network to project both the template and particles into the same feature space where the intra-class variations of positive training pairs are minimized and the interclass variations of negative training pairs are maximized simultaneously. Then, the candidate that is most similar to the template in the learned deep network is identified as the true target. Experiments on the benchmark data set including 51 challenging videos show that our DML tracker achieves a very competitive performance with the state-of-the-art trackers. [ABSTRACT FROM AUTHOR]