Consensus learning has been widely adopted in clustering tasks due to its robustness to noise and outliers, as well as its ability to aggregate diverse base results from multiple models. However, existing methods are often limited by feature alignment issues arising from heterogeneous feature dimensionalities and label permutation inconsistencies across models. To address these limitations, this paper introduces a novel Consensus Fuzzy Representation Learning (CFRL) framework. The CFRL framework initially employs various fuzzy clustering methods to generate diverse membership matrices, which are then transformed into affinity matrices to serve as base fuzzy representations. This transformation strategy not only effectively resolves feature alignment issues but also provides a unified processing mechanism for both single-view and multi-view data scenarios. To derive robust consensus features, the tensor Schatten$p$-norm encourages low-rank structure in the tensorized fuzzy representations, whereas an$l_{1}$-norm regularized error term captures and suppresses sparse noise. Moreover, a block diagonal regularizer is incorporated into the objective function, which guides the consensus feature matrix toward an optimal block diagonal structure. This structural constraint enhances cluster discriminability and enables reliable final cluster assignments. Comprehensive experimental evaluations validate that the proposed CFRL method achieves superior performance compared to state-of-the-art approaches.

The traditional particle swarm optimization algorithms have some shortcomings, such as low convergence precision, slow convergence speed, and susceptibility to falling into local optima when solving complex optimization problems. To address these issues, this paper proposes a new particle swarm optimization algorithm that incorporates teamwork. Specifically, we introduce the concept of teamwork, and divide the particles into multiple teams and selecting team leaders . The particles can fully utilize the team’s prompt information to guide the search process. The team leader updates the search direction of its particles through the generation of information factors, thus giving the algorithm better global search capabilities. The position and behavior of the team leader affect the search behavior of other particles, reducing the risk of falling into local optimal solutions. In addition, to further improve the algorithm’s efficiency, we propose adaptive adjustment of information factors and learning factors. This adaptive adjustment mechanism enables the algorithm to adjust parameters flexibly according to the characteristics of the problem and the current search state, thereby accelerating convergence speed and improving convergence precision. To verify the performance of the proposed algorithm, we make an empirical analysis on 27 different test functions, the shortest path problem and the optimal SINR value problem for UAV deployment. The experimental results show that the proposed algorithm has obvious advantages in convergence accuracy and convergence speed. Compared with other algorithms, this algorithm can find a better solution faster and converge to the global optimal solution more stably.

Discriminative correlation filter (DCF)-based tracking algorithms continue to advance in the field of UAV tracking due to their computational efficiency. The idea of integrating the advantages of historical information and response adjustments into the CF tracking framework is continuously being developed. However, maintaining the stability of mobile video tracking in highly dynamic environments is extremely challenging. This difficulty arises from frequent changes in targets and backgrounds, as well as the stochastic noise generated by the photon-counting process in sensors. In addition, the inconsistent rates of these changes are often overlooked and require further scrutiny. In this paper, we propose a dynamic sensitivity enhanced correlation filter with adaptive second-order difference spatial regularization to address the issue of inconsistent motion rates in dynamic videos. We use the non-local means algorithm to denoise template images before feature extraction, improving the discriminative power of target contours. Then, we incorporate the proposed dynamic-sensitivity error method into CF learning and employ a novel adaptive second-order difference spatial regularization to simultaneously optimize the filter coefficients and spatial regularization weights. This regularization effectively works in synergy with the dynamic-sensitivity error strategy. Furthermore, an additional ADMM optimizer is introduced to derive the solution, thereby improving the convergence and computational efficiency of the algorithm. This algorithm supports the adjustment of filter updates in dynamic environments by balancing consistency with previous filter templates and flexibility to accommodate rapid target changes. By conducting extensive experiments on three challenging UAV tracking databases, we compare the proposed model with existing models. The experimental results demonstrate our superior performance. Code is released at:https://github.com/Johnsonirene/LDECF.

Unmanned aerial vehicle (UAV) visual tracking has been a hot research topic in the field of remote sensing. Many filter-based UAV trackers have achieved excellent performance. However, existing methods do not distinguish the importance of different feature channels with semantic information and background information, which may hinder the tracker’s ability to adapt to changing environments. To deal with this problem, we propose a channel attentional correlation filters learning model (CACF). Specifically, we introduce the fuzzy C-means algorithm to pre-classify the extracted features and then perform weight penalty to feature channels with different membership degrees. In addition, the filter can adapt more effectively to the background’s rapid changes during the UAV tracking process by learning the second-order difference between adjacent three frame features. Finally, the comparative experiments are conducted on three mainstream UAV datasets, including DTB70, UAV123@10fps, and UAVDT. The experimental results demonstrate the effectiveness of the proposed method. The tracking performance of CACF surpasses that of other state-of-the-art trackers.

Unmanned Aerial Vehicles (UAV) visual tracking has always been a challenging task. Existing correlation filter tracking algorithms typically utilize the Histograms of Oriented Gradients (HOG) and Color Names (CN) method to directly incorporate the extracted target features into the model updating process. However, in low-resolution video quality, it leads to unstable target feature values. To address this limitation, we propose a novel preprocessing technique involving Gaussian denoising. This preprocessing step is designed to enhance the stability of the target’s feature values and make the target’s scale information clearer, thereby improving the tracker’s recognition capability for the target and effectively reducing noise interference. Furthermore, in contrast to other UAV trackers that rely on a singular representation of contextual information, this paper aims to enhance the utilization of historical information. Therefore, we introduce a context-based approach that integrates continuously weighted dynamic response maps from both temporal and spatial perspectives. Our tracker has the ability to adapt to rapid environmental changes during the tracking process while simultaneously reducing the potential risks of model overfitting and distortion. Extensive experiments are conducted on authoritative datasets, including DTB70, UAV123@10fps, and UAVDT, comparing our model against other advanced trackers. The experimental results validate the superior tracking performance and robustness of our tracker.

For a broad range of applications, hyperspectral image (HSI) classification is a hot topic in remote sensing, and convolutional neural network (CNN)-based methods are drawing increasing attention. However, to train millions of parameters in CNN requires a large number of labeled training samples, which are difficult to collect. A conventional Gabor filter can effectively extract spatial information with different scales and orientations without training, but it may be missing some important discriminative information. In this article, we propose the Gabor ensemble filter (GEF), a new convolutional filter to extract deep features for HSI with fewer trainable parameters. GEF filters each input channel by some fixed Gabor filters and learnable filters simultaneously, then reduces the dimensions by some learnable 1×1 filters to generate the output channels. The fixed Gabor filters can extract common features with different scales and orientations, while the learnable filters can learn some complementary features that Gabor filters cannot extract. Based on GEF, we design a network architecture for HSI classification, which extracts deep features and can learn from limited training samples. In order to simultaneously learn more discriminative features and an end-to-end system, we propose to introduce the local discriminant structure for cross-entropy loss by combining the triplet hard loss. Results of experiments on three HSI datasets show that the proposed method has significantly higher classification accuracy than other state-of-the-art methods. Moreover, the proposed method is speedy for both training and testing.

Tensor-based robust principal component analysis (PCA) methods are efficient to discover the low-rank part of a hyperspectral image for reducing redundant information and guarantee good classification results. However, current methods cannot remove noise adequately, and the residual noise remaining in the low-rank image limits the further improvement of classification performance. Thus, enhancing the robustness to noise is important and helpful for tensor-based robust PCA (RPCA) methods to process hyperspectral images. To this end, we propose a tensor-based RPCA method with a locality preserving graph and frontal slice sparsity (LPGTRPCA) for hyperspectral image classification. Specifically, a tensor$l_{2,2,1}$norm that requires the frontal slice sparsity of a tensor is defined to extract the noise in the hyperspectral image from the frontal direction. What is more, a position-based Laplacian graph that preserves the local structures of a tensor according to the spatial position is designed for relieving the impact of the residual noise remaining in the low-rank image. Based on the tensor nuclear norm, the tensor$l_{2,2,1}$norm, and the position-based Laplacian graph, LPGTRPCA efficiently separates the low-rank part with little noise from a raw hyperspectral image and achieves more robust classification results than current methods. LPGTRPCA is optimized by the alternative direction multiplier method (ADMM), and the convergence of solutions is experimentally demonstrated. In the experiments conducted on Indian Pines, Pavia University, and Salinas datasets, LPGTRPCA outperformed various state-of-the-art and classical tensor-based RPCA methods in terms of average class classification accuracy (AA), overall classification accuracy (OA), and kappa coefficient (KC).

Abstract Morphological edge detection is a principal component in pattern recognition and machine vision. Traditional edge detection operators only take pixel mutual into consideration. However, the edges are influenced not only by pixel mutual but also by the boundary characteristics. Here, the vector co‐occurrence morphological edge detection operator is proposed, which takes the pixel and boundary information both into consideration. The vector co‐occurrence algorithm is exploited to resist the influence of the noise points and detect the edges from the colour image rather than the grey image. And, we lead to define a precise definition of the manner of sorting high‐dimensional data for the colour image. The experiment results always illustrate the advancement and practicability of our methods against the baseline method. In terms of experiments, the BSDS500 dataset is introduced to compare and analyse with other algorithms. Based on the standard benchmark index evaluation in the BSDS500 dataset, the ODS and AP of various algorithms are compared and analysed.

Abstract This paper proposes an algorithm for the tensor completion problem of estimating multi‐linear data under the limitation of observation rate. Many tensor completion methods are based on nuclear norm minimization, they may fail to achieve the global solution for solving nuclear norm minimization in tensor completion problem with high missing ratio. To tackle this issue, an adaptive tensor completion method based on parallel multi‐block alternating direction method of multipliers (ADMM) algorithm is proposed, it can derive the model from the initial estimate and compute the next estimate from the current solution. The parallel multi‐block ADMM with global convergence is adopted to solve the dual problem, which greatly improves the processing power and reliability of the algorithm.

Unbalanced geometric structure caused by variations with deformations, rotations and outliers is a critical issue that hinders correspondence establishment between image pairs in existing graph matching methods. To deal with this problem, in this work, we propose a dual calibration mechanism (DCM) for establishing feature points correspondence in graph matching. In specific, we embed two types of calibration modules in the graph matching, which model the correspondence relationship in point and edge respectively. The point calibration module performs unary alignment over points and the edge calibration module performs local structure alignment over edges. By performing the dual calibration, the feature points correspondence between two images with deformations and rotations variations can be obtained. To enhance the robustness of correspondence establishment, the L2,p-norm is employed as the similarity metric in the proposed model, which is a flexible metric due to setting the different p values. Finally, we incorporate the dual calibration and L2,p-norm based similarity metric into the graph matching model which can be optimized by an effective algorithm, and theoretically prove the convergence of the presented algorithm. Experimental results in the variety of graph matching tasks such as deformations, rotations and outliers evidence the competitive performance of the presented DCM model over the state-of-the-art approaches.

Establishing correspondence between two given geometrical graph structures is an important problem in computer vision and pattern recognition. In this paper, we propose a robust graph matching (RGM) model to improve the effectiveness and robustness on the matching graphs with deformations, rotations, outliers, and noise. First, we embed the joint geometric transformation into the graph matching model, which performs unary matching over graph nodes and local structure matching over graph edges simultaneously. Then, the L2,1-norm is used as the similarity metric in the presented RGM to enhance the robustness. Finally, we derive an objective function which can be solved by an effective optimization algorithm, and theoretically prove the convergence of the proposed algorithm. Extensive experiments on various graph matching tasks, such as outliers, rotations, and deformations show that the proposed RGM model achieves competitive performance compared to the existing methods.

Unsupervised domain adaptation is effective in leveraging the rich information from the source domain to the unsupervised target domain. Though deep learning and adversarial strategy make an important breakthrough in the adaptability of features, there are two issues to be further explored. First, the hard-assigned pseudo labels on the target domain are risky to the intrinsic data structure. Second, the batch-wise training manner in deep learning limits the description of the global structure. In this paper, a Riemannian manifold learning framework is proposed to achieve transferability and discriminability consistently. As to the first problem, this method establishes a probabilistic discriminant criterion on the target domain via soft labels. Further, this criterion is extended to a global approximation scheme for the second issue; such approximation is also memory-saving. The manifold metric alignment is exploited to be compatible with the embedding space. A theoretical error bound is derived to facilitate the alignment. Extensive experiments have been conducted to investigate the proposal and results of the comparison study manifest the superiority of consistent manifold learning framework.

In the fields of industry research, infrared spectrometers are widely used in diverse applications. However, the spectrum often suffers from band overlap and random noise due to the distortion caused by the point spread function, especially for aging instruments. The problem of reconstructing the clear spectrum from the degraded spectrum is called spectrum deconvolution. Traditional partial differential equation (PDE) methods rely on distribution assumptions in the reconstructed process. This restriction makes PDE methods sensitive to tackle complex instrumental broadening effect in the dispersive IR spectrometers. Also, we need to spend much time setting the parameters of PDE models manually. These problems intuitively degrade the performances of PDE methods. In this article, we propose an end-to-end neural network framework for spectral deconvolution problem. The novelty of this article lies in its strong robustness from dilated deconvolution and self-paced learning procedure to challenge the complicated degraded spectra. Actually, the deconvolution problem is tailored to a dense prediction problem in this article. Inspired by the extensive use and excellent effects of dilated convolutions in dense prediction, a lightweight dilated convolution module is given to detect the overlaps of degraded spectra. Experimental results demonstrate that the proposed solution has an outstanding performance against many other approaches. Such improvements have the potential to facilitate industrial applications and further exploration of an unknown chemical mixture. Our framework has a good performance on feature extracting and spectrum reconstruction, even in the case of low signal-to-noise ratio.

Local feature descriptor plays a key role in different image classification applications. Some of these methods such as local binary pattern and image gradient orientations have been proven effective to some extent. However, such traditional descriptors which only utilize single-type features, are deficient to capture the edges and orientations information and intrinsic structure information of images. In this paper, we propose a kernel embedding multiorientation local pattern (MOLP) to address this problem. For a given image, it is first transformed by gradient operators in local regions, which generate multiorientation gradient images containing edges and orientations information of different directions. Then the histogram feature which takes into account the sign component and magnitude component, is extracted to form the refined feature from each orientation gradient image. The refined feature captures more information of the intrinsic structure, and is effective for image representation and classification. Finally, the multiorientation refined features are automatically fused in the kernel embedding discriminant subspace learning model. The extensive experiments on various image classification tasks, such as face recognition, texture classification, object categorization, and palmprint recognition show that MOLP could achieve competitive performance with those state-of-the art methods.