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arXiv 2020年

作者： Guo, Qingbei Wu, Xiao-Jun Kittler, Josef Feng, Zhiquan Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan Jinan250022 China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

Although group convolution operators are increasingly used in deep convolutional neural networks to improve the computational efficiency and to reduce the number of parameters, most existing methods construct their group convolution architectures by a predefined partitioning of the filters of each convolutional layer into multiple regular filter groups with an equal spatial group size and data-independence, which prevents a full exploitation of their potential. To tackle this issue, we propose a novel method of designing self-grouping convolutional neural networks, called SG-CNN, in which the filters of each convolutional layer group themselves based on the similarity of their importance vectors. Concretely, for each filter, we first evaluate the importance value of their input channels to identify the importance vectors, and then group these vectors by clustering. Using the resulting data-dependent centroids, we prune the less important connections, which implicitly minimizes the accuracy loss of the pruning, thus yielding a set of diverse group convolution filters. Subsequently, we develop two fine-tuning schemes, i.e. (1) both local and global fine-tuning and (2) global only fine-tuning, which experimentally deliver comparable results, to recover the recognition capacity of the pruned network. Comprehensive experiments carried out on the CIFAR-10/100 and ImageNet datasets demonstrate that our self-grouping convolution method adapts to various state-of-the-art CNN architectures, (Figure presented) Figure 1: Evolution of group convolutions. (a) Regular convolution. (b) Regular group convolution. (c) Permuting group convolution. (d) Learned group convolution. (d) Self-grouping convolution. Note that white channels represent the ignored input channels, and gray channels indicate the reused input channels. such as ResNet and DenseNet, and delivers superior performance in terms of compression ratio, speedup and recognition accuracy. We demonstrate the ability of SG-CNN

关键词： Convolution

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Contrastive learning based method for X-ray and CT registration under surgical equipment occlusion

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Computers in Biology and Medicine 2024年 180卷 108946页

作者： Wang, Xiyuan Zhang, Zhancheng Xu, Shaokang Luo, Xiaoqing Zhang, Baocheng Wu, Xiao-Jun School of Electronics and Information Engineering at University of Science and Technology Suzhou SuZhou215009 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence School of Artificial Intelligence and Computer Science at Jiangnan University WuXi214122 China Department of Orthopaedics General Hospital of Central Theater Command of PLA WuHan430012 China Shanghai Jirui Maestro Surgical Technology Co ShangHai200000 China

Deep learning-based 3D/2D surgical navigation registration techniques achieved excellent results. However, these methods are limited by the occlusion of surgical equipment resulting in poor accuracy. We designed a contrastive learning method that treats occluded and unoccluded X-rays as positive samples, maximizing the similarity between the positive samples and reducing interference from occlusion. The designed registration model has Transformer's residual connection (ResTrans), which enhances the long-sequence mapping capability, combined with the contrast learning strategy, ResTrans can adaptively retrieve the valid features in the global range to ensure the performance in the case of occlusion. Further, a learning-based region of interest (RoI) fine-tuning method is designed to refine the misalignment. We conducted experiments on occluded X-rays that contained different surgical devices. The experiment results show that the mean target registration error (mTRE) of ResTrans is 3.25 mm and the running time is 1.59 s. Compared with the state-of-the-art (SOTA) 3D/2D registration methods, our method offers better performance on occluded 3D/2D registration tasks. © 2024 Elsevier Ltd

关键词： Surgical equipment

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Learning a representation with the block-diagonal structure for pattern classification

arXiv

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arXiv 2019年

作者： Yin, He-Feng Wu, Xiao-Jun Kittler, Josef Feng, Zhen-Hua School of Internet of Things Engineering Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

Sparse-representation-based classification (SRC) has been widely studied and developed for various practical signal classification applications. However, the performance of a SRC-based method is degraded when both the training and test data are corrupted. To counteract this problem, we propose an approach that learns Representation with Block-Diagonal Structure (RBDS) for robust image recognition. To be more specific, we first introduce a regularization term that captures the block-diagonal structure of the target representation matrix of the training data. The resulting problem is then solved by an optimizer. Last, based on the learned representation, a simple yet effective linear classifier is used for the classification task. The experimental results obtained on several benchmarking datasets demonstrate the efficacy of the proposed RBDS method. The code will be released upon the acceptance of this paper. Copyright © 2019, The Authors. All rights reserved.

关键词： Image recognition

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Multiple riemannian manifold-valued descriptors based image set classification with multi-kernel metric learning

arXiv

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arXiv 2019年

作者： Wang, Rui Wu, Xiao-Jun Kittler, Josef School of Internet of Things Engineering Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

The importance of wild video based image set recognition is becoming monotonically increasing due to the large amount of video resources obtained by diversified video collection approaches, like surveillance, drive recorders, smart phones, and internet. However, the contents of these collected videos are often complicated, and how to efficiently perform set modeling and feature extraction is a big challenge for set-based classification algorithms. In recent years, some proposed image set classification methods have made a considerable advance by modeling the original image set with covariance matrix, linear subspace, or Gaussian distribution. Moreover, the distinctive geometry spanned by them are Symmetric Positive Definite (SPD) manifold, Grassmann manifold, and Gaussian embedded Riemannian manifold, respectively. As a matter of fact, most of them just adopt a single geometric model to describe each given image set, which may lose some other useful information for classification. To tackle this problem, we propose a novel algorithm to model each image set from a multi-geometric perspective. Specifically, the covariance matrix, linear subspace, and Gaussian distribution are applied for set representation simultaneously. In order to fuse these multiple heterogeneous Riemannian manifoldvalued features, the well-equipped Riemannian kernel functions are first utilized to map them into high dimensional Hilbert spaces. Then, a multi-kernel metric learning framework is devised to embed the learned hybrid kernels into a lower dimensional common subspace for classification. We conduct experiments on four widely used datasets corresponding to four different classification tasks: video-based face recognition, set-based object categorization, video-based emotion recognition, and dynamic scene classification, to evaluate the classification performance of the proposed algorithm. Extensive experimental results justify its superiority over the state-of-the-art. Copyright © 2019, Th

关键词： Vector spaces

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DreamNet: A Deep Riemannian Network based on SPD Manifold Learning for Visual Classification

arXiv

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arXiv 2022年

作者： Wang, Rui Wu, Xiao-Jun Chen, Ziheng Xu, Tianyang Kittler, Josef School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom School of Artificial Intelligence and Computer Science Jiangnan University China

Image set-based visual classification methods have achieved remarkable performance, via characterising the image set in terms of a non-singular covariance matrix on a symmetric positive definite (SPD) manifold. To adapt to complicated visual scenarios better, several Riemannian networks (RiemNets) for SPD matrix nonlinear processing have recently been studied. However, it is pertinent to ask, whether greater accuracy gains can be achieved by simply increasing the depth of RiemNets. The answer appears to be negative, as deeper RiemNets tend to lose generalization ability. To explore a possible solution to this issue, we propose a new architecture for SPD matrix learning. Specifically, to enrich the deep representations, we adopt SPDNet [1] as the backbone, with a stacked Riemannian autoencoder (SRAE) built on the tail. The associated reconstruction error term can make the embedding functions of both SRAE and of each RAE an approximate identity mapping, which helps to prevent the degradation of statistical information. We then insert several residual-like blocks with shortcut connections to augment the representational capacity of SRAE, and to simplify the training of a deeper network. The experimental evidence demonstrates that our DreamNet can achieve improved accuracy with increased depth of the network. Copyright © 2022, The Authors. All rights reserved.

关键词： Covariance matrix

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Convolutional Neural Network-Based Multimodal Image Information Fusion for Moisture Damage Assessment of Cultural Heritage Buildings

SSRN

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SSRN 2024年

作者： Wang, Fuzhi Huang, Jizhong Fu, Yu Department of Civil Engineering Shanghai University Shanghai200444 China Institute for the Conservation of Cultural Heritage Shanghai University Shanghai200444 China Key Laboratory of Silicate Cultural Relics Conservation Shanghai University Ministry of Education Shanghai200444 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China

Water is an essential factor causing the deterioration of historical and cultural heritage, and identifying and quantifying moisture damage is essential for conserving cultural heritage buildings. This study combines infrared thermography (IRT) and microwave technology to investigate moisture-induced diseases and the variation of the spatial distribution of water content in the Yungang Grottoes of China, a World Heritage Site. Information on building humidity distribution was extracted and combined from IRT and microwave information. Furthermore, multi-feature image fusion combines different types of monitoring information through the Convolutional Neural Network (CNN), which is used to diagnose moisture distribution and content accurately. The features obtained from different non-destructive methods were then jointly analyzed using statistical analysis methods such as absolute contrast, noise ratio (SNR), and spatial stability to quantify the artifact moisture defect data, thus improving the accuracy and range of features for detecting water-damaged pathologies. © 2024, The Authors. All rights reserved.

关键词： Convolution

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Adaptive multi-modal fusion hashing via hadamard matrix

arXiv

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arXiv 2020年

作者： Yu, Jun Zhang, Donglin Shu, Zhenqiu Chen, Feng The College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China The Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China The Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China The School of Computer Science and Technology Anhui University of Technology Ma'anshan China

Hashing plays an important role in information retrieval, due to its low storage and high speed of processing. Among the techniques available in the literature, multi-modal hashing, which can encode heterogeneous multi-modal features into compact hash codes, has received particular attention. Most of the existing multi-modal hashing methods adopt the fixed weighting factors to fuse multiple modalities for any query data, which cannot capture the variation of different queries. Besides, many methods introduce hyper-parameters to balance many regularization terms that make the optimization harder. Meanwhile, it is time-consuming and labor-intensive to set proper parameter values. The limitations may significantly hinder their promotion in real applications. In this paper, we propose a simple, yet effective method that is inspired by the Hadamard matrix. The proposed method captures the multi-modal feature information in an adaptive manner and preserves the discriminative semantic information in the hash codes. Our framework is flexible and involves a very few hyper-parameters. Extensive experimental results show the method is effective and achieves superior performance compared to state-of-the-art algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Hash functions

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A localization method avoiding flip ambiguities for micro-UAVs with bounded distance measurement errors

arXiv

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arXiv 2018年

作者： Guo, Qingbei Zhang, Yuan Lloret, Jaime Kantarci, Burak Seah, Winston K.G. Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan Shandong China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China Integrated Management Coastal Research Institute Universidad Politecnica de Valencia Spain School of Electrical Engineering and Computer Science University of Ottawa Canada School of Engineering and Computer Science Victoria University of Wellington New Zealand

Localization is a fundamental function in cooperative control of micro unmanned aerial vehicles (UAVs), but is easily affected by flip ambiguities because of measurement errors and flying motions. This study proposes a localization method that can avoid the occurrence of flip ambiguities in bounded distance measurement errors and constrained flying motions;to demonstrate its efficacy, the method is implemented on bilateration and trilateration. For bilateration, an improved bi-boundary model based on the unit disk graph model is created to compensate for the shortage of distance constraints, and two boundaries are estimated as the communication range constraint. The characteristic of the intersections of the communication range and distance constraints is studied to present a unique localization criterion which can avoid the occurrence of flip ambiguities. Similarly, for trilateration, another unique localization criterion for avoiding flip ambiguities is proposed according to the characteristic of the intersections of three distance constraints. The theoretical proof shows that these proposed criteria are correct. A localization algorithm is constructed based on these two criteria. The algorithm is validated using simulations for different scenarios and parameters, and the proposed method is shown to provide excellent localization performance in terms of average estimated error. Our code can be found at: https://***/QingbeiGuo/***. Copyright © 2018, The Authors. All rights reserved.

关键词： Measurement errors

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Affine Non-negative Collaborative Representation Based pattern Classification

arXiv

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arXiv 2020年

作者： Yin, He-Feng Wu, Xiao-Jun Feng, Zhen-Hua Kittler, Josef School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

—During the past decade, representation-based classification methods have received considerable attention in pattern recognition. In particular, the recently proposed non-negative representation based classification (NRC) method has been reported to achieve promising results in a wide range of classification tasks. However, NRC has two major drawbacks. First, there is no regularization term in the formulation of NRC, which may result in unstable solution and misclassification. Second, NRC ignores the fact that data usually lies in a union of multiple affine subspaces, rather than linear subspaces in practical applications. To address the above issues, this paper presents an affine non-negative collaborative representation (ANCR) model for pattern classification. To be more specific, ANCR imposes a regularization term on the coding vector. Moreover, ANCR introduces an affine constraint to better represent the data from affine subspaces. The experimental results on several benchmarking datasets demonstrate the merits of the proposed ANCR method. The source code of our ANCR is publicly available at https://***/yinhefeng/ANCR. Copyright © 2020, The Authors. All rights reserved.

关键词： Vectors

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Fabric defect detection method based on Cascade Deep Support Vector Data Description

引用

Journal of Physics: Conference Series 2020年第1期1651卷

作者： Xupeng Wang Yueyang Li Haichi Luo Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China College of Internet of Things Engineering Jiangnan University Wuxi China

The Fabric defect detection method based on Cascade Deep Support Vector Data Description (SVDD) is proposed in this paper. The method describes the data by Deep SVDD to realize the correct evaluation between the normal fabric images and the images with defects in the high dimensional space. Combining with the cascading method, the proposed algorithm can be used to detect minor defects more accurately and quickly. We take the anomaly score as the evaluation result of the testing image, then the Median Absolute Deviation (MAD) outlier detection method is applied to get the final defect detection results. This newly developed method can be trained with only a small amount of defect-free samples, which greatly reduces manual intervention. A variety types of defects in the general fabric images can be detected efficiently. The experimental results demonstrate that the proposed model has good global performance and high recall rate.

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