The Schatten-p norm, as a class of structure-inducing norms based on singular values, has been widely used to enhance model low-rankness and representation capability due to its flexibility in structural modeling and favorable mathematical properties. However, its potential in cluster distribution modeling has long been overlooked. Therefore, we explore the potential of maximizing the Schatten-p norm as a regularization strategy specifically designed to achieve balanced clustering. This work is the first to investigate its effectiveness in promoting cluster balance. To be specific, maximizing Schatten-p norm effectively guides the assignment of data points, ensuring a more balanced distribution of samples across clusters. We have conducted an in-depth theoretical analysis and validated its effectiveness through extensive clustering experiments. Experimental results demonstrate that, compared to existing methods, this regularization term significantly improves clustering quality and obtain reasonable clustering.

The multi-view clustering methods based on tensor regression can make full use of the potential structural information between views and achieve data-level fusion. However, existing tensor regression-based approaches for anchor graph often overlook the probabilistic nature of anchor graph, focusing solely on sample labels while ignoring the influence of anchor labels on clustering results. To overcome these limitations, we introduce Tensorized Label Learning via Balanced Tensor Regression (TLL-BTR). Our key idea is to exploit the probabilistic nature of the anchor graph by regarding the sample labels as a projection tensor that maps the anchor graph into the label space, thereby producing anchor labels. By enforcing constraints on these anchor labels, we guide the concurrent learning of sample labels and achieve co-label learning between anchors and samples. To prevent trivial solutions, we maximize the nuclear norm to promote an even distribution of samples across clusters. Extensive experiments on benchmark datasets demonstrate that TLL-BTR consistently outperforms state-of-the-art methods.

Vehicle re-identification plays a key role in intelligent transportation systems by supporting critical applications such as traffic flow optimization and public safety. With the introduction of triplet loss, the greatly enhanced vehicle discriminative ability brings more possibilities for the practical applications of vehicle re-identification. However, traditional triplet loss may not be fully suited for vehicle re-identification due to the risk of falling into local minima caused by solely selecting hard triplets. To address this problem, a Knowledge-Driven Multi-Branch Interaction Network (KMINet) is proposed which emphasizes robustness, feature diversity and feature fusion. At first, a knowledge-driven triplet mining module is introduced, which dynamically varies the selection of positive and negative samples. This module enables the network to learn the significance of features from a diverse range of triplets. Then, to tackle the challenge of viewpoint variation, a global and local multi-branch structure is designed to leverage both transformer and convolutional blocks to extract global context features and local detail features. This ensures that our model captures comprehensive vehicle characteristics. Finally, we incorporate an attention transfer module within the multi-branch structure to achieve information interaction and reduce feature redundancy across different branches. Our method outperforms state-of-the-art models on the VeRi-776 dataset, achieving 85.71% mAP and 97.73% CMC1. Moreover, the competitive performance is demonstrated on both VehicleID and VERI-Wild datasets, with mAP scores of 92.50% and 88.56%, respectively.

Recently, multi-view clustering methods have been widely used in handling multi-media data and have achieved impressive performances. Among the many multi-view clustering methods, anchor graph-based multi-view clustering has been proven to be highly efficient for large-scale data processing. However, most existing anchor graph-based clustering methods necessitate post-processing to obtain clustering labels and are unable to effectively utilize the information within anchor graphs. To address this issue, we draw inspiration from regression and feature selection to proposeAnchorGraph-BasedFeatureSelection forOne-stepMulti-ViewClustering (AGFS-OMVC). Our method combines embedding learning and sparse constraint to perform feature selection, allowing us to remove noisy anchor points and redundant connections in the anchor graph. This results in a clean anchor graph that can be projected into the label space, enabling us to obtain clustering labels in a single step without post-processing. Lastly, we employ the tensor Schatten$p$-norm as a tensor rank approximation function to capture the complementary information between different views, ensuring similarity between cluster assignment matrices. Experimental results on five real-world datasets demonstrate that our proposed method outperforms state-of-the-art approaches.

In this article, we present a novel general framework for incomplete multi-view clustering by integrating graph learning and spectral clustering. In our model, a tensor low-rank constraint are introduced to learn a stable low-dimensional representation, which encodes the complementary information and takes into account the cluster structure between different views. A corresponding algorithm associated with augmented Lagrangian multipliers is established. In particular, tensor Schatten p -norm is used as a tighter approximation to the tensor rank function. Besides, both consistency and specificity are jointly exploited for subspace representation learning. Extensive experiments on benchmark datasets demonstrate that our model outperforms several baseline methods in incomplete multi-view clustering.

Retinal fundus images reveal the condition of retina, blood vessels and optic nerve, and is becoming widely adopted in clinical work because any subtle changes to the structures at the back of the eyes can affect the eyes and indicate the overall health. Recently, machine learning, in particular deep learning by convolutional neural network (CNN), has been increasingly adopted for computer-aided detection (CAD) of retinal lesions. However, a significant barrier to the high performance of CNN based CAD approach is the lack of sufficient labeled image samples for training. Unlike the fully-supervised learning which relies on pixel-level annotation of pathology in fundus images, this paper presents a new approach to discriminate the location of various lesions based on image-level labels via weakly learning. More specifically, our proposed method leverages the multilevel feature maps and classification score to cope with both bright and red lesions in fundus images. To enhance capability of learning less discriminative parts of objects (e.g. small blobs of microaneurysms opposed to bulk of exudates), the classifier is regularized by refining images with corresponding labels. The experimental results of the performance evaluation and benchmarking at both image-level and pixel-level on the public DIARETDB1 dataset demonstrate the feasibility and excellent potentials of our method in practical usage.

This paper proposes an effective pore representation descriptor based on Convolutional Neural Networks (CNNs). We make full use of the diversity and large quantities of sweat pores in fingerprints to learn a deep feature, denoted as DeepPoreID. The DeepPoreID is then used to describe the local feature for each pore and finally integrated into the classical direct pore matching method. Experiments carried on the challenge public high-resolution fingerprint database with small image size of 320 × 240 shows the effectiveness of the proposed DeepPoreID. The results also have shown that the proposed method outperforms other existing state-of-the-art methods in the aspect of recognition accuracy. About ~35% rise in accuracy can be obtained when compared with the best result achieved by existing methods.

A fully automated system for retinal vessel tortuosity system diagnosis is proposed in this paper. Our diagnosis system includes: (1) automated retinal vessel segmentation and tracing; (2) computerized tortuosity grading. In recent years, many works have been done on computerized diagnosis of retinal vessel tortuosity. But there are few researchers working on a fully automated system. The major difficulties in producing a fully automated system includes: (1) automated tracing of vessels to identify each individual branch; (2) global tortuosity grading of a retinal vessel image. In this paper, we propose a scheme to trace and grade retinal vessels using scale (variant widths and lengths of vessel segments) dependent techniques. The experimental results show that our system is useful in clinical applications.

The detection of microaneurysms in digital color fundus photographs is a critical first step in automated screening for diabetic retinopathy (DR), a common complication of diabetes. To accomplish this detection numerous methods have been published in the past but none of these was compared with each other on the same data. In this work we present the results of the first international microaneurysm detection competition, organized in the context of the Retinopathy Online Challenge (ROC), a multiyear online competition for various aspects of DR detection. For this competition, we compare the results of five different methods, produced by five different teams of researchers on the same set of data. The evaluation was performed in a uniform manner using an algorithm presented in this work. The set of data used for the competition consisted of 50 training images with available reference standard and 50 test images where the reference standard was withheld by the organizers (M. Niemeijer, B. van Ginneken, and M. D. Abràmoff). The results obtained on the test data was submitted through a website after which standardized evaluation software was used to determine the performance of each of the methods. A human expert detected microaneurysms in the test set to allow comparison with the performance of the automatic methods. The overall results show that microaneurysm detection is a challenging task for both the automatic methods as well as the human expert. There is room for improvement as the best performing system does not reach the performance of the human expert. The data associated with the ROC microaneurysm detection competition will remain publicly available and the website will continue accepting submissions.

The early diagnosis of proliferative diabetic retinopathy (PDR), a common complication of diabetes that damages the retina, is crucial to the protection of the vision of diabetes sufferers. The onset of PDR is signaled by the appearance of neovascular net. Such neovascular nets might be identified using retinal vessel extraction techniques. The commonly used matched filter methods often produce false positive detections of neovascular nets due to their proneness to detect nonline edges as well as lines. In this paper, we propose a modified matched filter for retinal vessel extraction that applies a local vessel cross-section analysis using double-sided thresholding to reduce false responses to nonline edges. Our proposed modified matched filters demonstrated higher true positive rate and lesser false detection than existing matched-filter-based schemes in vessel extraction.

Automated line detection is a classical image processing topic with many applications such as road detection in remote images and vessel detection in medical images. Many traditional line detectors, such as Gabor filter, the second order derivative of Gaussian and Radon transform will response not only to lines but also to edges, e.g. they will give high responses to the edges of bright lines or blobs when only dark lines are required. To reduce false detections when extracting only dark (or bright) lines, in this paper we propose a line detector by using the first derivative of Gaussian. It can detect dark lines without much false detection on blobs or bright lines. Meanwhile, the proposed method can estimate line width simultaneously. Experiments on various images are performed to test the proposed algorithm.

This paper presents a new approach to automated retinal vessel segmentation based on multiscale analysis and adaptive thresholding. The accurate identification of the appearance of blood vessels in ocular fundus plays an important role in medical diagnosis of many diseases. In contrast to the existing methods for computer aided diagnosis which are either window-based or tracking based, we propose a novel scheme which combines multiscale analysis and adaptive thresholding to help eye care specialists to screen larger populations for vessel abnormalities under various conditions such as vessel size and local contrast. Our method includes a multiscale analytical scheme based on Gabor filters and scale multiplication, and adaptive thresholding. The experimental results demonstrate the feasibility and effectiveness of the proposed algorithms which are good for detecting large and small vessels concurrently with robustness to denoise and enhance the responses at low contrast.

Current holistic appearance based face recognition methods require a high dimensional feature space to attain fruitful performance. In this paper, we have proposed a relatively low feature dimensional, template-matching scheme to cope with the transformed appearance-based face recognition problem. We use aggregated Gabor filter responses to represent face images. We investigated the effect of "duplicate" images (images from different sessions) and the effect of facial expressions. Our results indicate that the proposed method is more robust in recognizing "duplicate" images with variations in facial expression than the principal component analysis method.