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Works316 from public data
- Wavelet Integrated CNNs for Noise-Robust Image Classification258
WaveCNets, the wavelet integrated versions of VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness than their vanilla versions.
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- Joint sparse principal component analysis203
A novel approach called joint sparse principal component analysis (JSPCA) is proposed to jointly select useful features and enhance robustness to outliers and the experimental results demonstrate that the proposed approach is feasible and effective.
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- Low-Rank Preserving Projections157
This paper proposes a novel dimensionality reduction method, named low-rank preserving projections (LRPP) for image classification, and shows the effectiveness and the feasibility of the proposed method with encouraging results.
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- Integrating Conventional and Inverse Representation for Face Recognition149
The experiments show that the proposed conventional and inverse representation-based linear regression classification (CIRLRC) can obtain very high accuracy and greatly outperforms the naive LRC and other state-of-the-art conventional representation based face recognition methods.
- Using the idea of the sparse representation to perform coarse-to-fine face recognition145
A coarse-to-fine face recognition method that identifies the classes that are ''far'' from the test sample and removes them from the set of the training samples and the classification problem becomes a simpler one with fewer classes.
- Structured optimal graph based sparse feature extraction for semi-supervised learning143
A novel structured optimal graph based sparse feature extraction (SOGSFE) method for semi-supervised learning is proposed, in which the local structure learning, sparse representation, and label propagation are simultaneously framed to perform data dimensionality reduction.
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- Regularized Label Relaxation Linear Regression121
A novel regularized label relaxation LR method is proposed, which relaxes the strict binary label matrix into a slack variable matrix by introducing a nonnegative label relaxation matrix into LR, which provides more freedom to fit the labels and simultaneously enlarges the margins between different classes as much as possible.
- Robust Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation114
This paper proposes a robust semi-supervised subspace clustering method based on non-negative LRR (NNLRR) and introduces an efficient linearized alternating direction method with adaptive penalty to solve the corresponding optimization problem.
- Face recognition by sparse discriminant analysis via joint L2,1-norm minimization114
Experiments on three standard face databases illustrate FLDA and SDA via L 2,1 -norm penalty term can significantly improve their recognition performance, and obtain inspiring results with low computation cost and for low-dimension feature.
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- Multigranulation rough-fuzzy clustering based on shadowed sets93
By integrating the notions of shadowed sets and multigranulation into rough-fuzzy clustering approaches, the overall topology of data can be captured well and the uncertain information implicated inData can be effectively addressed, including the uncertainty generated by fuzzification coefficient.
- Discriminative deep multi-task learning for facial expression recognition90
A discriminative DMTL (DDMTL) facial expression recognition method, which overcomes the above shortcomings by considering both the class label information and the samples’ local spatial distribution information simultaneously.
- Projected fuzzy C-means clustering with locality preservation90
An orthogonally projected space, which preserves the locality of structural properties, can be generated in LPFCM, thus enhancing the capability of fuzzy C-means (FCM) for handling high-dimensional data.
- A general tensor representation framework for cross-view gait recognition90
The results demonstrate that the performances of the proposed methods are superior to existing state-of-the-art cross-view gait recognition approaches.
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- Precise Facial Landmark Detection by Reference Heatmap Transformer85
This is the first study to explore how to enhance facial landmark detection by transforming the reference heatmap information and the experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in the literature.
- Learning a Nonnegative Sparse Graph for Linear Regression85
A novel nonnegative sparse graph (NNSG) learning method, in which the linear regression and graph learning were simultaneously performed to guarantee an overall optimum, showed that NNSG can obtain very high classification accuracy and greatly outperforms conventional G-SSL methods, especially some conventional graph construction methods.
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- GuidedStyle: Attribute knowledge guided style manipulation for semantic face editing78
A novel learning framework, called GuidedStyle, is proposed to achieve semantic face editing on pretrained StyleGAN by guiding the image generation process with a knowledge network, and allows an attention mechanism in StyleGAN generator to adaptively select a single layer for style manipulation.
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- Discriminative Invariant Alignment for Unsupervised Domain Adaptation73
A novel domain adaptation method, referred to as discriminative invariant alignment (DIA), for image representation, which enriches the knowledge matrix by combining the class discrim inative information of the source domain and local data structure information ofThe target domain into a new framework.
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- Principal Component Analysis based on Nuclear norm Minimization70
A novel robust method, called nuclear norm-based on PCA (N-PCA) is presented, developed under a novel unified framework of PCA to remedy the bias of computing data mean and the low-dimensional representation of a sample both of which are treated as unknown variables in a single model together with projection matrix.
- Adaptive Locality Preserving Regression70
The conventional ‘Frobenius norm’ is replaced with the special $l_{2,1}$ norm to constrain the projection, which enables the method to adaptively select the most important features from the original high-dimensional data for feature extraction and the negative influence of the redundant features and noises residing in the original data can be greatly eliminated.
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- Low-rank adaptive graph embedding for unsupervised feature extraction66
The proposed LRAGE is imposed with low-rank constraint for the sake of exploring the underlying correlation structure of data and learning more informative projection, and extensive experiments on five well-known face data sets and three non-face data sets demonstrate the superiority of the proposed L RAGE.
- Uncertainty-Guided Pixel Contrastive Learning for Semi-Supervised Medical Image Segmentation65
This work proposes a novel uncertainty-guided pixel contrastive learning method for semi-supervised medical image segmentation and proposes that the effective global representations learned by an image encoder should be equivariant to different geometric transformations.
- Robust Facial Landmark Detection by Multiorder Multiconstraint Deep Networks63
It is interesting to show that the proposed MMDN can generate more accurate boundary-adaptive landmark heatmaps and effectively enhance shape constraints to the predicted landmarks for faces with large pose variations and heavy occlusions.
- Enhanced robust spatial feature selection and correlation filter learning for UAV tracking62
This paper proposes an innovative method called enhanced robust spatial feature selection and correlation filter Learning (EFSCF), which performs jointly sparse feature learning to handle boundary effects effectively while suppressing the influence of background pixels and noises.
- Low-Rank 2-D Neighborhood Preserving Projection for Enhanced Robust Image Representation62
A novel NPP method called low-rank 2DNPP (LR-2DNPP) is proposed, which encodes the structural incoherence of the learned clean data to enhance the discriminative ability for feature extraction and uses seven public image databases to verify the performance of the proposed methods.
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- Computational Technologies for Fashion Recommendation: A Survey60
This article comprehensively review recent research efforts on fashion recommendation from a technological perspective and offers a deep insight into how the fashion industry could benefit from the computational technologies of fashion recommendation.
- Local graph embedding based on maximum margin criterion via fuzzy set60
A new efficient dimension reduction method—local graph embedding method based on maximum margin criterion via Fuzzy Set for face recognition based on fuzzy intrinsic graph and the fuzzy penalty graph is proposed.
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- Nonnegative Discriminant Matrix Factorization59
A novel method called nonnegative discriminant matrix factorization (NDMF) is proposed for image classification that integrates the nonnegative constraint, orthogonality, and discriminant information in the objective function and is proposed to enhance the discriminant ability of the learned base matrix.
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- The L2,1-norm-based unsupervised optimal feature selection with applications to action recognition57
This paper proposes a matrix-based feature selection and classification method that takes the advantage of L2,1-norm regularization and designs a novel classification framework that performs unsupervised optimal feature selection (UOFS) to simultaneously integrate dimensionality reduction, sparse representation, jointly sparse feature extraction and feature selection into a unified optimization objective.
- Granular maximum decision entropy-based monotonic uncertainty measure for attribute reduction54
A monotonic uncertainty measure, called granular maximum decision entropy (GMDE), is proposed and its monotonicity is theoretically proved, and a forward heuristic attribute reduction algorithm is provided, which could simultaneously select the informative attributes and remove the unnecessary attributes in the procedure of attribute reduction, thus resulting in high efficiency.
- Noisy low-tubal-rank tensor completion54
A TNN-based estimator is proposed to estimate a tensor from its partial noisy observations and a non-asymptotic upper bound on the estimation error is established and proved to be optimal (up to a logarithm factor) in a minimax sense.
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- Sparse two-dimensional local discriminant projections for feature extraction50
A novel method called sparse two-dimensional locality discriminant projections (S2DLDP), which is a sparse extension of graph-based image feature extraction method that can learn the sparse 2D face profile subspaces, which give an intuitive, semantic and interpretable feature subspace for face representation.
- Maximum decision entropy-based attribute reduction in decision-theoretic rough set model47
A novel monotonic uncertainty measure is introduced for attribute reduction in the decision-theoretic rough set model and a heuristic attribute reduction algorithm based on the maximum decision entropy is developed, which maximizes the relevance of the reduct to the class attribute and also minimizes the redundancy of the condition attributes within the reductor.
- Manifold transfer subspace learning based on double relaxed discriminative regression43
A new model framework for cross-domain image classification based on double relaxed discriminative regression based on MTSL-DRDR, which is evaluated on challenging benchmark datasets, and a large number of experiment results show the superiority of the proposed method.
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- Breast cancer discriminant feature analysis for diagnosis via jointly sparse learning42
Experimental results on breast cancer datasets indicate that JSDA outperforms some well-known subspace learning algorithms in prediction accuracy, not matter they are non-sparse or sparse, particularly in the cases of small sample sizes.
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- Supervised discrete discriminant hashing for image retrieval41
This paper proposed a novel supervised discrete discriminant hashing learning method, which can learn discrete hashing codes and hashing function simultaneously, and achieves leading performance compared with the state-of-the-art semi-supervised classification methods.
- Orthogonal self-guided similarity preserving projection for classification and clustering41
This paper proposes a unified learning framework to simultaneously obtain the optimal feature representation and intrinsic structure of data and demonstrates that the method outperforms state-of-the-art methods in DR and subspace clustering.
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- Linear Discriminant Analysis40
This chapter gives a brief introduction to Fisher’s linear discriminant (FLD), and derives the Foley-Sammon optimal discriminant vectors, which can be obtained by maximizing the Fisher discriminant criterion with the orthogonal constraints of the discriminant vector.
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- Robust principal component analysis via optimal mean by joint ℓ2,1 and Schatten p-norms minimization39
This paper proposes an optimization model constituted by a l2,1-norm based loss function and a Schatten p-norm regularization term that can pursue and remove outliers, exactly recover a low-rank matrix and calculate the optimal mean.
- Manifold discriminant regression learning for image classification38
A novel linear regression (LR) framework is proposed for image classification and two concrete algorithms are proposed under the framework, which are named manifold discriminant regression learning (MDRL) and robust manifold discriminative LSR (DLSR).
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- Robust sparse low-rank embedding for image dimension reduction36
A new dimensionality reduction method based on low-rank representation termed robust sparse low- rank embedding (RSLRE) is proposed and can achieve very encouraging results compared with some classical feature extraction methods.
- Principles for constructing three-way approximations of fuzzy sets: A comparative evaluation based on unsupervised learning36
In this study, the principles for constructing three-way approximations of fuzzy sets are summarized and Friedman testing followed by Holm-Bonferroni testing are exploited to test the performance significance of the proposed criteria.
- Robust Jointly Sparse Regression with Generalized Orthogonal Learning for Image Feature Selection35
A robust jointly sparse regression method to integrate the locality geometric structure with generalized orthogonality constraint and joint sparsity into a regression modal to address the problems of Ridge regression and its variants.
- Robust face alignment by cascaded regression and de-occlusion35
It is interesting to show that the cascaded deep generative regression model can effectively locate occlusions and recover more genuine faces, which can be further used to improve the performance of face alignment.
- Nuclear Norm-Based 2DLPP for Image Classification35
A novel locality preserving projection method called nuclear norm-based two-dimensional locality preserving projections (NN-2DLPP), which recovers the noisy data matrix through low-rank learning and removes noise in data, and keeps data points belonging to the same class as close to each other as possible in the new projective subspace.
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- Multi-scale gist feature manifold for building recognition31
The results show that the proposed model is superior to other models in practice of building recognition and can handle the building recognition problem caused by rotations, variant lighting conditions and occlusions very well.
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- Robust and Precise Facial Landmark Detection by Self-Calibrated Pose Attention Network29
It is shown that by integrating the BALI fields and SCPA model into a novel SCPAN, more facial prior knowledge can be learned and the detection accuracy and robustness of the method for faces with large poses and heavy occlusions have been improved.
- Projective robust nonnegative factorization29
A framework named Projective Robust Nonnegative Factorization (PRNF) is proposed in this paper for robust image feature extraction and classification and results are compared with state-of-the-art robust NMF-based algorithms.
- Two-dimensional color uncorrelated discriminant analysis for face recognition29
Experimental results show that the proposed algorithm performs better than other color face recognition methods and the two-dimensional color uncorrelated discriminant features are more effective for low-resolution image compared with conventional gray-based features.
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- Image decomposition based matrix regression with applications to robust face recognition27
This work aims to simplify the complex images and propose a simple and robust matrix regression based classification model, called ID-NMR, which performs favorably compared with state-of-the-art regression based Classification methods.
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- Light-Aware Contrastive Learning for Low-Light Image Enhancement26
A Light-Aware Contrastive Regularization (LACR) based on contrastive learning is designed to exploit information from both clear and low-light images, which yields a significant enhancement in the National Aeronautics and Space Administration (NASA) testset.
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- Discriminative low-rank projection for robust subspace learning23
This paper proposes a novel model named Discriminative Low-Rank Projection (DLRP), which can break the limitation of the small class problem which means the number of projections is bound by thenumber of classes.
- Robust facial landmark detection by cross-order cross-semantic deep network23
By integrating the CTM module and COCS regularizer, the proposed CCDN can effectively activate and learn more fine and complementary cross-order cross-semantic features to improve the accuracy of facial landmark detection under extremely challenging scenarios.
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- Target redirected regression with dynamic neighborhood structure22
A general regression framework called RLRR is proposed, where a low-rank constraint is imposed on regression matrices to explore the underlying correlation structures of classes to derive an equivalent solution to the resultant optimization problems with non-convex constraints.
- Rough possibilistic C-means clustering based on multigranulation approximation regions and shadowed sets22
This study presents a rough possibilistic C-means clustering approach based on multigranulation approximation regions and shadowed sets, which can handle the uncertainties implicated in data and generated by the model parameters simultaneously.
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- Adaptive linear discriminant regression classification for face recognition21
This paper proposes an adaptive linear discriminant regression classification algorithm (ALDRC) that makes use of different weights to characterize the different contributions of the training samples and utilizes such weighting information to calculate the between-class and the within-class reconstruction errors, and seeks to find an optimal projection matrix.
- K-local hyperplane distance nearest neighbor classifier oriented local discriminant analysis21
This paper embeds the decision rule of HKNN classifier into the discriminant analysis model to develop a new feature extractor, called K-local hyperplane distance nearest neighbor classifier oriented local discriminantAnalysis (HOLDA), in which a regularization item is imposed on the original HKNN algorithm to obtain a more reliable distance metric.
- Feature extraction based on fuzzy local discriminant embedding with applications to face recognition21
The fuzzy local discriminant embedding (FLDE) algorithm is proposed, in which the fuzzy k -nearest neighbour (FKNN) is implemented to reduce these outer effects to obtain the correct local distribution information to persuit good performance.
- Discriminative dual-stream deep hashing for large-scale image retrieval20
A discriminative dual-stream deep hashing (DDDH) method, which integrates the pairwise similarity loss and the classification loss into a unified framework to take full advantage of label information.
- Constrained shadowed sets and fast optimization algorithm20
The continuous and convex properties of Pedrycz's optimization objective function to construct shadowed sets, as well as the existence and uniqueness of solution points, are analyzed in detail and it is demonstrated that different approximation region partitions would be generated even under the same optimization model.
- Constrained three-way approximations of fuzzy sets: From the perspective of minimal distance20
In this paper, the essential mathematical properties of the objective function used as a criterion to construct three-way approximations of fuzzy sets based on the principle of minimal distance, as well the characteristics of the optimal solutions, are analyzed.
- Generalized Discriminant Local Median Preserving Projections (GDLMPP) for Face Recognition20
The theoretical analysis shows that GDLMPP is equivalent to DLMPP when the within-class scatter matrix is non-singular, and extensive experiments prove that the proposed algorithm can provide a better representation and achieve higher face recognition rates than previous approaches.
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- Locality preserving embedding for face and handwriting digital recognition18
A novel supervised method, called locality preserving embedding (LPE), is proposed to feature extraction and dimensionality reduction and shows that LPE consistently outperforms classical linear methods.
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- Bilinear Supervised Hashing Based on 2D Image Features17
A bil inear supervised discrete hashing method based on 2D image features which utilizes bilinear projections to binarize the image matrix features such that the intrinsic characteristics in the2D image space are preserved in the learned binary codes.
- Local sparsity preserving projection and its application to biometric recognition17
In LSPP, a training sample could be more possibly represented by training samples from the same class and a more accurate sparse reconstructive weight matrix is obtained and has more powerful discriminative ability than traditional dimensionality reduction methods.
- Local sparse representation projections for face recognition17
Comprehensive comparison and extensive experiments show that LSRP achieves higher recognition rates than principle component analysis, linear discriminant analysis and the state-of-the-art techniques such as LPP, SPP and maximum variance projections.
- Feature extraction using two-dimensional local graph embedding based on maximum margin criterion17
A novel method for image feature extraction, namely the two-dimensional local graph embedding, which is based on maximum margin criterion and thus not necessary to convert the image matrix into high-dimensional image vector and directly avoid computing the inverse matrix in the discriminant criterion is proposed.
- Fuzzy local maximal marginal embedding for feature extraction17
An improved algorithm called fuzzy local maximal marginal embedding (FLMME) for linear dimensionality reduction is presented, which significantly differing from the existing graph-based algorithms is that two novel fuzzy gradual graphs are constructed in FLMME.
- Precise facial landmark detection by Dynamic Semantic Aggregation Transformer16
By integrating the DSA model and DSS model into the proposed DSAT, which outperforms state-of-the-art models in the literature in both dynamic architecture and dynamic parameter manners, more specialized features can be learned for achieving more precise face alignment.
- A Closer Look at Classifier in Adversarial Domain Generalization16
CloCls proposes a new method called auxiliary classifier in adversarial domain generalization, which exhibits sufficient generalization ability and outperforms state-of-the-art DG methods and improves the diversity of source domains by introducing auxiliary classifiers.
- Active AU Based Patch Weighting for Facial Expression Recognition16
The problem of multiclass expression recognition is converted into triplet-wise expression recognition, and a new feature optimization model based on action unit (AU) weighting and patch weight optimization is proposed to represent the specificity of the expression triplet.
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- Textile Fabric Defect Detection Based on Improved Faster R-CNN15
The proposed improved Faster R-CNN model with Region Proposal Network (RPN), Soft Non-Maximum Suppression (NMS), and Region of Interest (ROI) Align can achieve a detection accuracy of 98% and an mean of Average Precision (mAP) of 85%, which is more competitive than the state-of-the-art deep learning-based object detection algorithms.
- Low-rank discriminative regression learning for image classification15
A novel regression learning method which named low-rank discriminative regression learning (LDRL) for image representation, which assumes that the input data is corrupted and thus the L1 norm can be used as a sparse constraint on the noised matrix to recover the clean data for regression, which can improve the robustness of the algorithm.
- Pedestrian re-Identification Based on Tree Branch Network with Local and Global Learning15
Experimental results demonstrate the effectiveness of the global and local feature learning method in the proposed TBN framework and show significant improvement in performance over state-of-the-art methods on three public benchmarks: Market-1501, CUHK-03 and DukeMTMC.
- Case study of 3D fingerprints applications15
3D finger point cloud data is collected by Structured-light Illumination method and the newly defined distinctive 3D shape ridge feature can be used for personal authentication with Equal Error Rate (EER) of ~8.3%.
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- Dual robust regression for pattern classification13
A dual robust regression framework (DualRR) for pattern classification is presented, demonstrating the merits of the proposed model over state-of-the-art regression-based classification methods.
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- Tongue size and shape classification fusing segmentation features for traditional Chinese medicine diagnosis12
This work proposed an efficient deep network, TSC-WNet, for tongue size and shape classification, which requires fewer parameters than classic CNNs like AlexNet, VGG16 and ResNet18, and achieves better classification performance.
- Robust jointly sparse embedding for dimensionality reduction12
A robust and sparse dimensionality reduction method based on linear reconstruction, called Robust Jointly Sparse Embedding (RJSE), is proposed by introducing L2, 1-norm as the basic measurement and regularization term and design a simple iterative algorithm to obtain the optimal solution.
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- Sparse local discriminant projections for discriminant knowledge extraction and classification11
The experimental results show that SLDP can give reasonable semantic results and achieves competitive performance compared with some techniques such as PCA, LPP, neighbourhood preserving embedding (NPE) and the recently proposed unified sparse subspace learning (USSL).
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- Binary sparse signal recovery algorithms based on logic observation10
Two kinds of recovery algorithms are developed and they are inspired from the matching pursuit method and Bayesian method, respectively and it is hoped that the proposed theories and algorithms can make contribution to the related applications of pattern recognition.
- Robust Locally Discriminant Analysis via Capped Norm10
A novel method called robust locally discriminant analysis via capped norm (RLDA) is proposed, which shows the superior performance of RLDA to other compared dimensionality reduction methods.
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- Multi-Manifold Locality Graph Embedding Based on the Maximum Margin Criterion (MLGE/MMC) for Face Recognition9
This work introduced a new algorithm named “multi-manifold locality graph embedding algorithm based on the maximum margin criterion” (MLGE/MMC) by combining the ideas of themaximum margin criterion (M MC) and multiple manifolds.
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- Fisher Difference Discriminant Analysis: Determining the Effective Discriminant Subspace Dimensions for Face Recognition8
Comprehensive comparison and extensive experiments show that FDDA is superior to some state-of-the-art techniques in face recognition and robust to noise and invariant to rotations, rescalings and translations of the data.
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- Graph embedding discriminant analysis for face recognition7
The experimental results show that GEDA consistently outperforms LDA and MFA when the training sample size per class is small, and is applied to face recognition and is examined on the Yale, ORL and AR face image databases.
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- Maximal local interclass embedding with application to face recognition6
A new dimensionality reduction approach called maximal local interclass embedding (MLIE) is developed, which can be viewed as a linear approach of a multimanifolds-based learning framework, in which the information of neighborhood is integrated with the local inter class relationships.
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- Nuclear norm regularized coding with local position-patch and nonlocal similarity for face hallucination4
This paper proposes to directly use the image matrix based regression model for combination coefficients computation to preserve the essential structural information of the input patch matrix to improve the hallucination performance.
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- Think About Boundary: Fusing Multi-level Boundary Information for Landmark Heatmap Regression3
A two-stage but end-to-end approach for exploring the relationship between the facial boundary and landmarks to get boundary-aware landmark predictions, which consists of two modules: the self-calibrated boundary estimation (SCBE) module and the boundary- Aware landmark transform (BALT) module.
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- Sparse embedding visual attention system combined with edge information3
A sparse embedding visual attention system combined with edge information, which is described as a hierarchical model in this paper, and results show that this model outperforms other visual attention computational models.
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- Saliency-Based Person Re-identification by Probability Histogram2
An effective feature representation based on salient regions and a pool of multiple metric learning for person re-identification and Experimental results show that the proposed method outperforms others.
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- Optimal representation set construction with a competitive scheme for palmprint and face recognition2
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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