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Infrared and visible image fusion with ResNet and zero-phase component analysis

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arXiv 2018年

作者： Li, Hui Wu, Xiao-Jun Durrani, Tariq S. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China Department of Electronic and Electrical Engineering University of Strathclyde GlasgowG1 1XW United Kingdom

Feature extraction and processing are key tasks in the Image fusion algo- rithm, while most of deep learning-based methods use deep features directly without feature processing. This leads to the fusion performance degradation in some cases. To solve this drawback, in this paper, a novel fusion frame- work based on deep features and zero-phase component analysis (ZCA) is pro- posed. Firstly, the residual network (ResNet) is used to extract deep features from source images. Then ZCA and l1-norm are utilized to normalize the deep features and obtain initial weight maps. The final weight maps are obtained by employing a soft-max operation in association with the initial weight maps. Finally, the fused image is reconstructed using a weighted-averaging strategy. Compared with the existing fusion methods, experimental results demonstrate that the proposed algorithm achieves better performance in both objective assessment and visual quality. The code of our fusion algorithm is available at https://***/hli1221/imagefusion-resnet50. Copyright © 2018, The Authors. All rights reserved.

关键词： Infrared imaging

MoNET: No-reference image quality assessment based on a multi-depth output network

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Journal of Electronic Imaging 2021年第4期30卷 043007-043007页

作者： Sang, Qingbing Su, Chenfei Zhu, Lingying Liu, Lixiong Wu, Xiaojun Bovik, Alan C. Jiangnan University School of Artificial Intelligence and Computer Science Wuxi China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Wuxi China Beijing Institute of Technology School of Computer Science and Technology Beijing China University of Texas at Austin Laboratory for Image and Video Engineering AustinTX United States

When deep convolutional neural networks perform feature extraction, the features computed at each layer express different abstractions of visual information. The earlier layers extract highly compact low-level features such as bandpass and directional primitives, whereas deeper layers extract structural features of increasing abstraction, similar to contours, shapes, and edges, becoming less effable as the depth increases. We propose a different kind of end-to-end no-reference (NR) image quality assessment (IQA) model, which is defined as a multi-depth output convolutional neural network (MoNET). It accomplishes this by mapping both shallow and deep features to perceived quality. MoNET delivers three outputs that express shallow (lower-level) and deep (high-level) features, and maps them to subjective quality scores. The multiple outputs are combined into a single, final quality score. MoNET does this by combining the responses of three learning machines, so it may be viewed as a form of ensemble learning. The experimental results on three public image quality databases show that our proposed model achieves better performance than other state-of-the-art NR IQA algorithms. © 2021 SPIE and IS and T.

关键词： Convolutional neural networks

Non-negative representation based discriminative dictionary learning for face recognition

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arXiv 2019年

作者： Chen, Zhe Wu, Xiao-Jun Kittler, Josef Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence School of IoT Engineering Jiangnan University Wuxi214122 China CVSSP University of Surrey GuildfordGU2 7XH United Kingdom

In this paper, we propose a non-negative representation based discriminative dictionary learning algorithm (NRDL) for multicategory face classification. In contrast to traditional dictionary learning methods, NRDL investigates the use of non-negative representation (NR), which contributes to learning discriminative dictionary atoms. In order to make the learned dictionary more suitable for classification, NRDL seamlessly incorporates non-negative representation constraint, discriminative dictionary learning and linear classifier training into a unified model. Specifically, NRDL introduces a positive constraint on representation matrix to find distinct atoms from heterogeneous training samples, which results in sparse and discriminative representation. Moreover, a discriminative dictionary encouraging function is proposed to enhance the uniqueness of class-specific sub-dictionaries. Meanwhile, an inter-class incoherence constraint and a compact graph based regularization term are constructed to respectively improve the discriminability of learned classifier. Experimental results on several benchmark face data sets verify the advantages of our NRDL algorithm over the state-of-the-art dictionary learning methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition

Class-specific residual constraint non-negative representation for pattern classification

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arXiv 2019年

作者： Yin, He-Feng Wu, Xiao-Jun Jiangnan University School of Internet of Things Engineering No. 1800 Lihu Avenue Wuxi214122 China Jiangsu Provincial Laboratory of Pattern Recognition and Computational Intelligence No. 1800 Lihu Avenue Wuxi214122 China

Representation based classification method (RBCM) remains one of the hottest topics in the community of pattern recognition, and the recently proposed non-negative representation based classification (NRC) achieved impressive recognition results in various classification tasks. However, NRC ignores the relationship between the coding and classification stages. Moreover, there is no regularization term other than the reconstruction error term in the formulation of NRC, which may result in unstable solution leading to misclassification. To overcome these drawbacks of NRC, in this paper, we propose a class-specific residual constraint non-negative representation (CRNR) for pattern classification. CRNR introduces a class-specific residual constraint into the formulation of NRC, which encourages training samples from different classes to competitively represent the test sample. Based on the proposed CRNR, we develop a CRNR based classifier (CRNRC) for pattern classification. Experimental results on several benchmark datasets demonstrate the superiority of CRNRC over conventional RBCM as well as the recently proposed NRC. Moreover, CRNRC works better or comparable to some state-of-the-art deep approaches on diverse challenging pattern classification tasks. The source code of our proposed CRNRC is accessible at https://***/yinhefeng/CRNRC. Copyright © 2019, The Authors. All rights reserved.

关键词： pattern recognition

Enhanced Long-Term Prediction Based on 2d Tensorization for Building Energy Consumption

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SSRN

SSRN 2023年

作者： Kong, Jun Deng, Song Jiang, Min Zhuang, Danfeng Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China IBISC Univ. Evry Université Paris-Saclay 91020 France

Building energy consumption constitutes a significant contributor to greenhouse gas emissions. Accurate prediction of building energy consumption is crucial for building energy management, thus contributing to the reduction of green-house gas emissions. However, existing prediction algorithms often focus on within-period variations in energy con-sumption data and overlook between-period variations. This limitation makes it challenging to achieve precise predic-tion results, especially for long-term forecasts. To address this research gap, this paper proposes an Enhanced Long-Term Prediction based on 2D Tensorization (ELP2T) for building energy consumption. First, a Frequency-Guided 2D Tensorization Network (FG-2TN) is proposed. Energy consumption data, when represented as a one-dimensional time series, faces certain limitations, and FG-2TN is employed to address these limitations. Second, a Pro-gress-Optimized Deep Convolutional Network (PO-DCN) is proposed. It is designed to efficiently extract and learn features from the obtained two-dimensional tensors with fewer parameters and less time.. Third, A modular method is proposed to transform one-dimensional time series into two-dimensional tensors using Fast Fourier transform. These tensors are processed and then concatenated into a one-dimensional sequence for output. Final, a comparative analy-sis was conducted with various conventional prediction algorithms to demonstrate the superiority of the ELP2T model. The obtained average R² score for our proposed method is 0.807, representing an impressive enhancement of 11.06% compared to the most advanced alternatives. This substantial improvement firmly establishes the superiority of our approach. Especially, when considering a prediction length of 720 units, the performance gain of this metric increases to 18.09%, underscoring the pronounced advantage of our method in addressing long-term forecasting scenarios. © 2023, The Authors. All rights reserved.

关键词： Fast Fourier transforms

Face recognition via compact second order image gradient orientations

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arXiv 2022年

作者： Yin, He-Feng Wu, Xiao-Jun Song, Xiao-Ning Jiangnan University School of Artificial Intelligence and Computer Science No. 1800 Lihu Avenue Wuxi214122 China Jiangsu Provincial Laboratory of Pattern Recognition and Computational Intelligence No. 1800 Lihu Avenue Wuxi214122 China

Conventional subspace learning approaches based on image gradient orientations only employ the first-order gradient information. However, recent researches on human vision system (HVS) uncover that the neural image is a landscape or a surface whose geometric properties can be captured through the second order gradient information. The second order image gradient orientations (SOIGO) can mitigate the adverse effect of noises in face images. To reduce the redundancy of SOIGO, we propose compact SOIGO (CSOIGO) by applying linear complex principal component analysis (PCA) in SOIGO. Combined with collaborative representation based classification (CRC) algorithm, the classification performance of CSOIGO is further enhanced. CSOIGO is evaluated under real-world disguise, synthesized occlusion and mixed variations. Experimental results indicate that the proposed method is superior to its competing approaches with few training samples, and even outperforms some prevailing deep neural network based approaches. The source code of CSOIGO is available at https://***/yinhefeng/SOIGO. © 2022, CC BY-SA.

关键词： Face recognition

Dual Attention MobDenseNet(DAMDNet) for robust 3D face alignment

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arXiv 2019年

作者： Jiang, Lei Wu, Xiao-Jun Kittler, Josef School of IoT Engineering Jiangnan University Wuxi214122 China University of Surry GuildfordGU27XH United Kingdom Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China

3D face alignment of monocular images is a crucial process in the recognition of faces with disguise.3D face reconstruction facilitated by alignment can restore the face structure which is helpful in detcting disguise *** paper proposes a dual attention mechanism and an efficient end-to-end 3D face alignment *** build a stable network model through Depthwise Separable Convolution, Densely Connected Convolutional and Lightweight Channel Attention Mechanism. In order to enhance the ability of the network model to extract the spatial features of the face region, we adopt Spatial Group-wise Feature enhancement module to improve the representation ability of the network. Different loss functions are applied jointly to constrain the 3D parameters of a 3D Morphable Model (3DMM) and its 3D vertices. We use a variety of data enhancement methods and generate large virtual pose face data sets to solve the data imbalance problem. The experiments on the challenging AFLW,AFLW2000-3D datasets show that our algorithm significantly improves the accuracy of 3D face alignment. Our experiments using the field DFW dataset show that DAMDNet exhibits excellent performance in the 3D alignment and reconstruction of challenging disguised *** model parameters and the complexity of the proposed method are also reduced *** code is publicly available at https://***/LeiJiangJNU/DAMDNet Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

A Riemannian Residual Learning Mechanism for SPD Network

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A Riemannian Residual Learning Mechanism for SPD Network

International Joint Conference on Neural Networks (IJCNN)

作者： Zhenyu Cai Rui Wang Tianyang Xu Xiaojun Wu Josef Kittler School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Wuxi China Centre for Vision Speech and Signal Processing University of Surrey Guildford U.K.

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The generalization of Euclidean network paradigm to the Riemannian manifolds has attracted much attention for offering useful geometric representations in processing manifold-valued data in recent years. However, the information degradation during data compression mapping hinders Riemannian networks from going deeper, and there are very few solutions specifically designed for this problem. Given the remarkable success of deep Residual learning in Euclidean networks, a novel Riemannian residual learning mechanism (RRLM) is proposed in the context of Symmetric Positive Definite (SPD) manifolds, enabling the characterization of deep spatiotemporal features while preserving the manifold properties. Based on RRLM, a stack of SPD manifold-constrained residual-like blocks is designed on the tail of the original SPDNet(backbone) for the sake of conducting deep Riemannian residual learning. For simplicity, we refer to the network architecture introduced above as Riemannian residual SPD network (ResSPDNet). The experimental results achieved on three types of visual classification tasks, i.e., facial emotion recognition, drone recognition, and action recognition, demonstrate that our method can achieve improved accuracy with a deepened network structure.

关键词： Manifolds Learning systems Degradation Emotion recognition Visualization Accuracy Face recognition

Low-rank representations with incoherent dictionary for face recognition

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arXiv 2019年

作者： Xie, Pei Yin, He-Feng Wu, Xiao-Jun Jiangsu GuoGuang Electronic Information Technology Co. Ltd. Changzhou213015 China School of Internet of Things Engineering Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China

Face recognition remains a hot topic in computer vision, and it is challenging to tackle the problem that both the training and testing images are corrupted. In this paper, we propose a novel semi-supervised method based on the theory of the low-rank matrix recovery for face recognition, which can simultaneously learn discriminative low-rank and sparse representations for both training and testing images. To this end, a correlation penalty term is introduced into the formulation of our proposed method to learn an incoherent dictionary. Experimental results on several face image databases demonstrate the effectiveness of our method, i.e., the proposed method is robust to the illumination, expression and pose variations, as well as images with noises such as block occlusion or uniform noises. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition