Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. By dissecting the involved components in developing a person Re-ID system, we categorize it into the closed-world and open-world settings. The widely studied closed-world setting is usually applied under various research-oriented assumptions, and has achieved inspiring success using deep learning techniques on a number of datasets. We first conduct a comprehensive overview with in-depth analysis for closed-world person Re-ID from three different perspectives, including deep feature representation learning, deep metric learning and ranking optimization. With the performance saturation under closed-world setting, the research focus for person Re-ID has recently shifted to the open-world setting, facing more challenging issues. This setting is closer to practical applications under specific scenarios. We summarize the open-world Re-ID in terms of five different aspects. By analyzing the advantages of existing methods, we design a powerful AGW baseline, achieving state-of-the-art or at least comparable performance on twelve datasets for FOUR different Re-ID tasks. Meanwhile, we introduce a new evaluation metric (mINP) for person Re-ID, indicating the cost for finding all the correct matches, which provides an additional criteria to evaluate the Re-ID system for real applications. Finally, some important yet under-investigated open issues are discussed.

Deep Learning for Person Re-identification: A Survey and Outlook

We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a “flat” partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.

Density-based clustering based on hierarchical density estimates

Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Metric learning aims to measure the similarity among samples while using an optimal distance metric for learning tasks. Metric learning methods, which generally use a linear projection, are limited in solving real-world problems demonstrating non-linear characteristics. Kernel approaches are utilized in metric learning to address this problem. In recent years, deep metric learning, which provides a better solution for nonlinear data through activation functions, has attracted researchers’ attention in many different areas. This article aims to reveal the importance of deep metric learning and the problems dealt with in this field in the light of recent studies. As far as the research conducted in this field are concerned, most existing studies that are inspired by Siamese and Triplet networks are commonly used to correlate among samples while using shared weights in deep metric learning. The success of these networks is based on their capacity to understand the similarity relationship among samples. Moreover, sampling strategy, appropriate distance metric, and the structure of the network are the challenging factors for researchers to improve the performance of the network model. This article is considered to be important, as it is the first comprehensive study in which these factors are systematically analyzed and evaluated as a whole and supported by comparing the quantitative results of the methods.

Deep Metric Learning: A Survey

Abstract Class imbalance is often a problem in various real-world data sets, where one class (i.e. the minority class) contains a small number of data points and the other (i.e. the majority class) contains a large number of data points. It is notably difficult to develop an effective model using current data mining and machine learning algorithms without considering data preprocessing to balance the imbalanced data sets. Random undersampling and oversampling have been used in numerous studies to ensure that the different classes contain the same number of data points. A classifier ensemble (i.e. a structure containing several classifiers) can be trained on several different balanced data sets for later classification purposes. In this paper, we introduce two undersampling strategies in which a clustering technique is used during the data preprocessing step. Specifically, the number of clusters in the majority class is set to be equal to the number of data points in the minority class. The first strategy uses the cluster centers to represent the majority class, whereas the second strategy uses the nearest neighbors of the cluster centers. A further study was conducted to examine the effect on performance of the addition or deletion of 5 to 10 cluster centers in the majority class. The experimental results obtained using 44 small-scale and 2 large-scale data sets revealed that the clustering-based undersampling approach with the second strategy outperformed five state-of-the-art approaches. Specifically, this approach combined with a single multilayer perceptron classifier and C4.5 decision tree classifier ensembles delivered optimal performance over both small- and large-scale data sets.

分群式取樣法於類別不平衡問題之研究;Clustering-Based Under-sampling in Class Imbalanced Data

Crack detection on concrete bridges is a critical task to ensure bridge safety. However, many cracks on concrete bridges show low contrast and blurry edges in practice, which brings challenges to image-based crack detection. In this article, to improve the detection accuracy of blurred cracks, we propose the HDCB-Net—a deep learning-based network with the hybrid dilated convolutional block (HDCB) for the pixel-level crack detection. Specifically, HDCB is employed to expand the receptive field of the convolution kernel without increasing the computational complexity and to avoid the gridding effect generated by the dilated convolution. Meanwhile, to achieve a reasonable efficiency/accuracy tradeoff, the HDCB-Net only contains a few downsampling stages, which can avoid the loss of blurred crack pixels due to excessive downsampling. Furthermore, a two-stage strategy is proposed to realize the fast crack detection in a massive number of images (more than 100 000) with the high resolution (5120 × 5120 pixels). At the first stage, YOLOv4 is employed to filter out images without cracks and generate coarse region proposals. At the second stage, to achieve refined damage analysis, the HDCB-Net is used to detect pixel-level cracks from the coarse region proposals. The experimental results demonstrate that the proposed HDCB-Net is genetic and able to improve the detection accuracy of blurred cracks, and our two-stage strategy is efficient for fast crack detection. The whole detection process takes only 0.64 s to handle a single image. Additionally, we have established a public dataset, including 150 632 high-resolution images, dedicated to the research of crack detection, which have been released along with this article.

HDCB-Net: A Neural Network With the Hybrid Dilated Convolution for Pixel-Level Crack Detection on Concrete Bridges

Adaptive Cell Segmentation and Tracking for Volumetric Confocal Microscopy Images of a Developing Plant Meristem

Shoot apical meristems (SAMs) of higher plants harbor stem-cell niches. The cells of the stem-cell niche are organized into spatial domains of distinct function and cell behaviors. A coordinated interplay between cell growth dynamics and changes in gene expression is critical to ensure stem-cell homeostasis and organ differentiation. Exploring the causal relationships between cell growth patterns and gene expression dynamics requires quantitative methods to analyze cell behaviors from time-lapse imagery. Although technical breakthroughs in live-imaging methods have revealed spatio-temporal dynamics of SAM-cell growth patterns, robust computational methods for cell segmentation and automated tracking of cells have not been developed. Here we present a local graph matching-based method for automated-tracking of cells and cell divisions of SAMs of Arabidopsis thaliana. The cells of the SAM are tightly clustered in space which poses a unique challenge in computing spatio-temporal correspondences of cells. The local graph-matching principle efficiently exploits the geometric structure and topology of the relative positions of cells in obtaining spatio-temporal correspondences. The tracker integrates information across multiple slices in which a cell may be properly imaged, thus providing robustness to cell tracking in noisy live-imaging datasets. By relying on the local geometry and topology, the method is able to track cells in areas of high curvature such as regions of primordial outgrowth. The cell tracker not only computes the correspondences of cells across spatio-temporal scale, but it also detects cell division events, and identifies daughter cells upon divisions, thus allowing automated estimation of cell lineages from images captured over a period of 72 h. The method presented here should enable quantitative analysis of cell growth patterns and thus facilitating the development of in silico models for SAM growth.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-313X.2009.04117.x

Automated tracking of stem cell lineages of Arabidopsis shoot apex using local graph matching.

The classification of wood types plays an important role in many fields, especially in construction industry and furniture manufacturing. In order to manufacture rubber wood furniture with highly uniform color and texture, wood boards of different colors and textures should be classified elaborately. Many traditional methods have been applied in wood classification relying on extracting features using handcrafted descriptors designed by experienced experts, but it is not easy to construct robust features in various conditions. In this article, we present a split-shuffle-residual (SSR)-based CNN that can learn features automatically from wood images for real-time classification of rubber wood boards. Specifically, we introduce an SSR module that combines channel split and shuffle operations with residual structure to reduce the computation cost while maintaining high classification accuracy. In each module, the input is split into two low-dimensional branches, and the channel shuffle operation is used to enable the information communication between the input and the two separated branches, which is regarded as the feature reuse that enlarges network capacity without increasing complexity. The comprehensive experiments demonstrate that our algorithm outperforms other traditional classification methods and the state-of-the-art deep learning classification networks, yielding an accuracy of 94.86%. Furthermore, the analysis of running time indicates that the SSR-based CNN can be employed for wood classification in real time, which takes only 26.55 ms to handle a single image.

Real-Time Classification of Rubber Wood Boards Using an SSR-Based CNN

Most person re-identification methods, being supervised techniques, suffer from the burden of massive annotation requirement. Unsupervised methods overcome this need for labeled data, but perform poorly compared to the supervised alternatives. In order to cope with this issue, we introduce the problem of learning person re-identification models from videos with weak supervision. The weak nature of the supervision arises from the requirement of video-level labels, i.e. person identities who appear in the video, in contrast to the more precise frame-level annotations. Towards this goal, we propose a multiple instance attention learning framework for person re-identification using such video-level labels. Specifically, we first cast the video person re-identification task into a multiple instance learning setting, in which person images in a video are collected into a bag. The relations between videos with similar labels can be utilized to identify persons, on top of that, we introduce a co-person attention mechanism which mines the similarity correlations between videos with person identities in common. The attention weights are obtained based on all person images instead of person tracklets in a video, making our learned model less affected by noisy annotations. Extensive experiments demonstrate the superiority of the proposed method over the related methods on two weakly labeled person re-identification datasets.

Min Liu

Papers

HDCB-Net: A Neural Network With the Hybrid Dilated Convolution for Pixel-Level Crack Detection on Concrete Bridges

Adaptive Cell Segmentation and Tracking for Volumetric Confocal Microscopy Images of a Developing Plant Meristem

Automated tracking of stem cell lineages of Arabidopsis shoot apex using local graph matching.

Real-Time Classification of Rubber Wood Boards Using an SSR-Based CNN

Learning Person Re-Identification Models From Videos With Weak Supervision