Metric Learning For Person Re-identification Research Articles

Semi-supervised LDA and Multi-distance Metric Learning for Person Re-identification

The problem of person re-identification has attracted a lot of attention in the field of machine vision. In practice, the non-overlapping sample images change drastically and the sample size is small, which makes the metric model overfitting phenomenon. In this paper, based on the k-NN and the sample normality property, we propose a resampling linear discriminant analysis (LDA) algorithm to suppress the local constraints caused by small samples, then train it to obtain the person re-identification metric learning model. A semi-supervised LDA algorithm with semi-supervised characteristics is developed by optimizing the inter-class scatter for weighting. A joint distance metric-based approach is also proposed to learn both the Mahalanobis distance and Euclidean distance. The improved algorithm is tested on the VIPeR and CUHK01 datasets, and the results indicate that, despite the change in the total number of training samples, the algorithm in this paper shows high recognition accuracy.

A joint structure of multi-distance based metric learning for person re-identification

Person re-identification problem is a valuable computer vision task, which aims at matching pedestrian images of different cameras in a non-overlapping surveillance network. Existing metric learning based methods address this problem by learning a robust distance function. These methods learn a mapping subspace by forcing the distance of the positive pair much smaller than the negative pair by a strict constraint. The metric model is over-fitting to the training dataset. Due to drastic appearance variations, the handcrafted features are weak of representation for person re-identification. To address these problems, we propose a joint distance measure based approach, which learns a Mahalanobis distance and a Euclidean distance with a novel feature jointly. The novel feature is represented with a dictionary representation based method which considers pedestrian images of different camera views with the same dictionary. The joint distance combine the Mahalanobis distance based on metric learning method with the Euclidean distance based on the novel feature to measure the similarity between matching pairs. Extensive experiments are conducted on the publicly available bench marking datasets VIPeR and CUHK01. The identification results show that the proposed method reaches a good performance than the comparison methods.

HOB-net: high-order block network via deep metric learning for person re-identification

HOB-net: high-order block network via deep metric learning for person re-identification

Transductive semi-supervised metric learning for person re-identification

Semi-supervised learning is important and has become more widespread because obtaining labeled data is expensive and labor-intensive. In this paper, we focus on the challenging semi-supervised person Re-identification (ReID) task, which is a metric learning problem based on the assumption that unlabeled data is open-set. To address this problem, we propose the Transductive Semi-Supervised Metric Learning (TSSML) framework. In TSSML, we propose a graph-based transductive hard mining method for deeply mining hard triplets in unlabeled data and a degree-based relationship confidence scoring method for further reducing incorrect triplets. Moreover, we investigate the feature consistency loss (FCL) and adopt the curriculum learning strategy to improve the representation learning for semi-supervised ReID. Extensive experiments have been conducted on three large-scale ReID datasets and demonstrate the effectiveness of our TSSML framework.

Semisupervised Consistent Projection Metric Learning for Person Reidentification.

Person reidentification is a hot topic in the computer vision field. Many efforts have been paid on modeling a discriminative distance metric. However, existing metric-learning-based methods are a lack of generalization. In this article, the poor generalization of the metric model is argued as the biased estimation problem that the independent identical distribution hypothesis is not valid. The verification experimental result shows that there is a sharp difference between the training and test samples in the metric subspace. A semisupervised consistent projection metric-learning method is proposed to ease the biased estimation problem by learning a consistent constrained metric subspace in which the identified pairs are forced to follow the distribution of the positive training pairs. First, a semisupervised method is proposed to generate potential matching pairs from the k -nearest neighbors of test samples. The potential matching pairs are used to estimate the distances' distribution center of the positive test pairs. Second, the metric subspace is improved by forcing this estimation to be close to the center of the positive training pairs. Finally, extensive experiments are conducted on five datasets and the results demonstrate that the proposed method reaches the best performance, especially on the rank-1 identification rate.

An Adaptive Multi-Projection Metric Learning for Person Re-Identification Across Non-Overlapping Cameras

Person re-identification is one of the most important and challenging problems in video analytics systems; it aims to match people across non-overlapping camera views. For person re-identification, metric learning is introduced to improve the performance by providing a metric adapted for cross-view matching. The essence of metric learning is to search for an optimal projection matrix to project the original features into a new feature space. However, most existing metric learning methods overlook the inconsistency of feature distributions in multiple cameras. In this paper, we propose a multi-projection metric learning (MPML) method to overcome the inconsistency among multiple cameras in person re-identification. Our solution is to jointly learn multiple projection matrices using paired samples from different cameras to project features from different cameras into a common feature space. To make our method adaptive to newly added cameras without affecting the learned projection matrices, we further propose an adaptive MPML method, which can learn new camera projection matrices without having to update any of the obtained projection matrices. The proposed methods are evaluated on four major person re-identification data sets, with comprehensive experiments showing the effectiveness of the proposed methods and notable improvements over the state-of-the–art approaches.

Discriminative Structural Metric Learning for Person Reidentification in Visual Internet of Things

Recently, visual Internet of Things (VIoT) has been deployed in many critical missions that are related to society security, such as anti-terrorism, abnormal event detection, crisis monitoring surveillance, etc. In this paper, we focus on a key fundamental problem in VIoT, person reidentification, which aims to correlate people with appropriate labels. The same person captured by various visual sensors in VIoT appears different significantly. To effectively measure the similarity between image pairs, we propose a novel method named discriminative structural metric learning (DSML), which utilizes intraregion metric, weak extra-region metric and extra-region metric to fully mine the structural information of pedestrian in a local way. According to DSML, we obtain a vector where each element is a local similarity score between two subregions. In order to aggregate the local similarity scores into a global one, we further propose a novel aggregating similarity score method named discriminative subregion aggregating (DSA). The DSA could learn the discriminative subregion by assigning different weights for each subregion. The experimental results demonstrate that the proposed method achieves better performance than the state-of-the-art methods.

Joint deep semantic embedding and metric learning for person re-identification

We focus on the person re-identification (re-id) task, whose goal is to automatically re-identify individual persons from multiple non-overlapping cameras or the same camera across time. While most existing works rely on exploring properties of the visual data, we consider taking advantage of both visual and textual representations. Given images and natural language descriptions of the persons in the probe, the re-id system is required to rank all the samples in the gallery set. We embed the visual representations and textual descriptions in a unified space, in which we map similar examples close to each other and map dissimilar examples farther apart. Our premise is that, in general, strong semantic correlations exist between different persons. The space casts a person in gallery set as a combination of the persons in probe set. The model is trained in an end-to-end fashion. We conduct extensive experiments on the challenging i-LIDS, PRID-2011, CUHK03 and Market-1501 datasets, and confirm that the proposed model achieves state-of-the-art performances.

Accelerated low-rank sparse metric learning for person re-identification

Person re-identification is an open and challenging problem in computer vision. A surge of effort has been spent design the best feature representation, and to learn either the transformation of such features across cameras or an optimal matching metric. Metric learning solutions which are currently in vogue in the field generally require a dimensionality reduction pre-processing stage to handle the high-dimensionality of the adopted feature representation. Such an approach is suboptimal and a better solution can be achieved by combining such a step in the metric learning process. Towards this objective, a low-rank matrix which projects the high-dimensional vectors to a low-dimensional manifold with a discriminative Euclidean distance is introduced. The goal is achieved with a stochastic accelerated proximal gradient method. Experiments on two public benchmark datasets show that better performances than state-of-the-art methods are achieved.

Dual Regularized KISS Metric Learning for Person Reidentification

Dual Regularized KISS Metric Learning for Person Reidentification

WITHDRAWN: Cross-scenario transfer metric learning for person re-identification

WITHDRAWN: Cross-scenario transfer metric learning for person re-identification

Semi-supervised Region Metric Learning for Person Re-identification

In large-scale camera networks, label information for person re-identification is usually not available under a large amount of cameras due to expensive human labor efforts. Semi-supervised learning could be employed to train a discriminative classifier by using unlabeled data and unmatched image pairs (negatives) generated from non-overlapping camera views, but existing methods suffer from the problem of imbalanced unlabeled data. In this context, this paper proposes a novel semi-supervised region metric learning method to improve person re-identification performance under imbalanced unlabeled data. Firstly, instead of seeking for matched image pairs (positives) from the unlabeled data, we propose to estimate positive neighbors by label propagation with cross person score distribution alignment. Secondly, multiple positive regions are generated using sets of positive neighbors to learn a discriminative region-to-point metric. Experimental results demonstrate that the superiority of the proposed method over existing unsupervised, semi-supervised and person re-identification methods.

Unsupervised metric learning for person re-identification by image re-ranking

Person re-identification is an important and challenging problem in video surveillance. Most of the methods are based on supervised distance metrics learning, which have to label samples for distance metrics learning, while it is hardly done in massive cameras. This paper carried out an unsupervised distance metric learning method based on image re-ranking. This method calculates original distance matrix for samples from two cameras using original distance metric function, and re-ranking distance matrix to acquire a better distance function, then new distance rank matrix is calculated. This matrix is used to label positive and negative samples automatically, doing unsupervised distance metric learning, and acquire a better Mahalanobis distance metric function, without the need to manually label samples. Experiments are done on public datasets, and result is evaluated by CMC, which indicates this algorithm could overcome the difficulties of labelling massive samples, with a better re-identification rate than other algorithms.

Unsupervised metric learning for person re-identification by image re-ranking

Unsupervised metric learning for person re-identification by image re-ranking

Equidistance constrained metric learning for person re-identification

Person re-identification (re-id), aiming to search a specific person among a non-overlapping camera network, has attracted plenty of interest in recent years. This task is highly challenging, especially when there exists only single image per person in the database. In this paper, we present an algorithm for learning a Mahalanobis distance for person re-identification. Our method has two distinctive features: (1) to obtain the best separability of the training data, we first minimize the intra-class distances to the most extent by forcing intra-class distances to be zero, and (2) to promote the generalization ability of the learned metric, we then maximize the minimum margin between different classes. Inspired by the simple geometric intuition that a regular simplex maximizes its minimum side length, provided the sum of all side length is fixed, our method, called EquiDistance constrained Metric Learning (EquiDML), applies least-square regression technique to map images of the same person to the same vertex of a regular simplex, and images of different persons to different vertices of a regular simplex. Consequently, under the learned metric, images of the same class are collapsed to a single point, while images of different classes are transformed to be equidistant. This simple motivation is further formulated as a convex optimization problem, solved by the projected gradient descent method and proved to be very effective in person re-identification task. Although it is fairly simple, our method outperforms the state-of-the-art methods on CUHK01, CUHK03, Market1501 and DukeMTMC-reID datasets, and achieves very competitive performance on the widely used VIPeR dataset.

Kernelized random KISS metric learning for person re-identification

Abstract Person re-identification is critical for human tracking in the video surveillance which has attracted more and more attention in recent years. Various recent approaches have made great progress in re-identification performance using metric learning techniques and among them, Keep It Simple and Straightforward (KISS) metric learning method has shown remarkably importance because of its simpleness and high-efficiency. The KISS method is based on an assumption that the differences between feature pairs obey the Gaussian distribution. However, for most existing features of person re-identification, the distributions of differences between feature pairs are irregular and undulant. Therefore, prior to the Guassian based metric learning step, it's important to augment the Guassian distribution of data without losing discernment. Moreover, most metric learning methods were greatly influenced by the small sample size (SSS) problem and the KISS method is no exception, which causing the inexistence of inverse of covariance matrices. To solve the above two problems, we present Kernelized Random KISS (KRKISS) metric learning method. By transforming the original features into kernelized features, the differences between feature pairs can better fit the Gaussian distribution and thus they can be more suitable for the Guassian assumption based models. To solve the inverse of covariance matrix estimation problem, we apply a random subspace ensemble method to obtain exact estimation of covariance matrix by randomly selecting and combining several different subspaces. In each subspace, the influence of SSS problem can be minimized. Experimental results on three challenging person re-identification datasets demonstrate that the KRKISS method significantly improves the KISS method and achieves better performance than most existing metric learning approaches.

Beyond Pairwise Matching: Person Reidentification via High-Order Relevance Learning.

Person reidentification has attracted extensive research efforts in recent years. It is challenging due to the varied visual appearance from illumination, view angle, background, and possible occlusions, leading to the difficulties when measuring the relevance, i.e., similarities, between probe and gallery images. Existing methods mainly focus on pairwise distance metric learning for person reidentification. In practice, pairwise image matching may limit the data for comparison (just the probe and one gallery subject) and yet lead to suboptimal results. The correlation among gallery data can be also helpful for the person reidentification task. In this paper, we propose to investigate the high-order correlation among the probe and gallery data, not the pairwise matching, to jointly learn the relevance of gallery data to the probe. Recalling recent progresses on feature representation in person reidentification, it is difficult to select the best feature and each type of feature can benefit person description from different aspects. Under such circumstances, we propose a multihypergraph joint learning algorithm to learn the relevance in corporation with multiple features of the imaging data. More specifically, one hypergraph is constructed using one type of feature and multiple hypergraphs can be generated accordingly. Then, the learning process is conducted on the multihypergraph structure, and the identity of a probe is determined by its relevance to each gallery data. The merit of the proposed scheme is twofold. First, different from pairwise image matching, the proposed method jointly explores the relationships among different images. Second, multimodal data, i.e., different features, can be formulated in the multihypergraph structure, which can convey more information in the learning process and can be easily extended. We note that the proposed method is a general framework to incorporate with any combination of features, and thus is flexible in practice. Experimental results and comparisons with the state-of-the-art methods on three public benchmarking data sets demonstrate the superiority of the proposed method.

Joint dictionary and metric learning for person re-identification

Matching people across non-overlapping camera views, known as person re-identification, is of great importance for long-term pedestrian tracking in smart surveillance systems. Among various algorithms for person re-identification, dictionary learning is frequently utilized to build robust feature representation for images across different camera views. Metric learning, on the other hand, is usually exploited to find optimal feature subspace that maximizes the inter-person divergence while minimizes the intra-person divergence. Although both representative features and discriminative metrics have great impacts on the performance of person re-identification methods, most of the existing algorithms focus on only one of the two aspects. In this paper, by explicitly modeling discriminative metric learning into the dictionary learning procedure, we propose to formulate robust feature representation learning and discriminative metric learning into a unified framework. To alleviate the issue of amount bias towards hard negative pairs in metric learning, instance selection is conducted for hard negative mining during the similarity constraint formulation. Besides, we come up with closed-form solutions for dictionary and coefficients update. Extensive experiments on three challenging datasets as well as the cross datasets experiments demonstrate the effectiveness and generalization ability of the joint dictionary and metric learning framework.

Ring-push metric learning for person reidentification

Person reidentification (re-id) has been widely studied because of its extensive use in video surveillance and forensics applications. It aims to search a specific person among a nonoverlapping camera network, which is highly challenging due to large variations in the cluttered background, human pose, and camera viewpoint. We present a metric learning algorithm for learning a Mahalanobis distance for re-id. Generally speaking, there exist two forces in the conventional metric learning process, one pulling force that pulls points of the same class closer and the other pushing force that pushes points of different classes as far apart as possible. We argue that, when only a limited number of training data are given, forcing interclass distances to be as large as possible may drive the metric to overfit the uninformative part of the images, such as noises and backgrounds. To alleviate overfitting, we propose the ring-push metric learning algorithm. Different from other metric learning methods that only punish too small interclass distances, in the proposed method, both too small and too large inter-class distances are punished. By introducing the generalized logistic function as the loss, we formulate the ring-push metric learning as a convex optimization problem and utilize the projected gradient descent method to solve it. The experimental results on four public datasets demonstrate the effectiveness of the proposed algorithm.

Feedback mechanism based iterative metric learning for person re-identification

Person re-identification problem is targeting to match people in the views of non-overlapped camera networks. It is an important task in the fields of computer vision and video surveillance. It shows great value in applications like surveillance and action recognition. Existing metric learning based methods measure the similarity of sample pairs by learning a metric space in which the positive pairs are closer than negative pairs. However, the appearance features undergo with drastic variation. Person re-identification is a typical small sample problem. It is hard to learn a robust projection of metric subspace that takes all the situations into consideration. The learned metric subspace is usually over-fitting to training dataset due to the strict metric learning constraint. And the hard pairs in training dataset will weaken the discrimination of matching pairs’ similarity. To address these problems, a feedback mechanism based iterative metric learning method is proposed. The proposed method introduces a mean distance of multi-metric subspace to deal with the over-fitting problem. The joint discriminant optimal model on feedback top ranks matching pairs will enhance the discrimination of matching pairs’ similarity. It is a robust and discriminative distance metric which measures the matching pairs similarity with distances of multiple metric projections learned by a set of training datasets. Aiming to learn the multi-metric subspace, the proposed method gives a feedback mechanism based approach which back propagates the top ranks identification results as pseudo training datasets. The effectiveness of proposed mean distance of multi-metric projection is analyzed and proved theoretically. And extensive experiments on three challenging datasets, VIPeR, GRID and CUHK01 are conducted. The results show that the proposed method achieves the best performance and improves the state-of-the-art rank-1 identification rates by 18.48%, 2.00% and 5.41% on three datasets respectively.