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Cross-modal subspace learning via Kernel correlation maximization and discriminative structure preserving

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arXiv 2019年

作者： Yu, Jun Wu, Xiao-Jun School of IoT Engineering Jiangnan University Wuxi214122 China The Jiangsu Provincial Engineering Laboratory of Pattern Recognition Computational Intelligence Jiangnan University Wuxi214122 China

The measure between heterogeneous data is still an open problem. Many research works have been developed to learn a common subspace where the similarity between different modalities can be calculated directly. However, most of existing works focus on learning a latent subspace but the semantically structural information is not well preserved. Thus, these approaches cannot get desired results. In this paper, we propose a novel framework, termed Cross-modal subspace learning via Kernel correlation maximization and Discriminative structure-preserving (CKD), to solve this problem in two aspects. Firstly, we construct a shared semantic graph to make each modality data preserve the neighbor relationship semantically. Secondly, we introduce the Hilbert-Schmidt Independence Criteria (HSIC) to ensure the consistency between feature-similarity and semantic-similarity of samples. Our model not only considers the inter-modality correlation by maximizing the kernel correlation but also preserves the semantically structural information within each modality. The extensive experiments are performed to evaluate the proposed framework on the three public datasets. The experimental results demonstrated that the proposed CKD is competitive compared with the classic subspace learning methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

Unsupervised concatenation hashing via combining subspace learning and graph embedding for cross-Modal image retrieval

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arXiv 2019年

作者： Yu, Jun Wu, Xiao-Jun School of IoT Engineering Jiangnan University Wuxi214122 China The Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China

Different from the content-based image retrieval methods, cross-modal image retrieval methods uncover the rich semantic-level information of social images to further understand image contents. As multiple modal data depict a common object from multiple perspectives, many works focus on learning the unified subspace representation. Recently, hash representation has received much attention in the retrieval field. In common Hamming space, how to directly preserve the local manifold structure among objects become an interesting problem. Most of the unsupervised hashing methods attempt to solve it by constructing a neighborhood graph on every modality respectively. However, it is hard to decide the weight factor of each graph to get the optimal graph. To overcome this problem, we adopt the concatenated features to represent the common object since the information implied by different modalities is complementary. In our framework, Locally Linear Embedding and Locality Preserving Projection are introduced to reconstruct the manifold structure of the original space. Besides, The 2,1-norm constraint is imposed on the projection matrices to explore the discriminative hashing functions. Extensive experiments are performed on three public datasets and the experimental results show that our method outperforms several classic unsupervised hashing models. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data

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arXiv 2023年

作者： Bi, Chenyang Li, Yueyang Luo, Haichi Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China College of Internet of Things Engineering Jiangnan University 1800 Lihu Avenue Jiangsu Wuxi China

Unsupervised anomaly detection methods are at the forefront of industrial anomaly detection efforts and have made notable progress. Previous work primarily used 2D information as input, but multi-modal industrial anomaly detection based on 3D point clouds and RGB images is just beginning to emerge. The regular approach involves utilizing large pre-trained models for feature representation and storing them in memory banks. However, the above methods require a longer inference time and higher memory usage, which cannot meet the real-time requirements of the industry. To overcome these issues, we propose a lightweight dual-branch reconstruction network(DBRN) based on RGB-D input, learning the decision boundary between normal and abnormal examples. The requirement for alignment between the two modalities is eliminated by using depth maps instead of point cloud input. Furthermore, we introduce an importance scoring module in the discriminative network to assist in fusing features from these two modalities, thereby obtaining a comprehensive discriminative result. DBRN achieves 92.8% AUROC with high inference efficiency on the MVTec 3D-AD dataset without large pre-trained models and memory banks. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

Real-time instance segmentation based on contour learning

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Journal of Measurement Science and Instrumentation 2024年第3期15卷 328-337页

作者： GE Rui LIU Dengfeng ZHOU Haojie CHAI Zhilei WU Qin School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China Jiangsu Provincial Engineering Laboratory Pattern Recognition and Computational Intelligence Jiangnan UniversityWuxi 214122China

Instance segmentation plays an important role in image *** Deep Snake algorithm based on contour iteration deforms an initial bounding box to an instance contour end-to-end,which can improve the performance of instance segmentation,but has defects such as slow segmentation speed and sub-optimal initial *** solve these problems,a real-time instance segmentation algorithm based on contour learning was ***,ShuffleNet V2 was used as backbone network,and the receptive field of the model was expanded by using a 5×5 convolution ***,a lightweight up-sampling module,multi-stage aggregation(MSA),performs residual fusion of multi-layer features,which not only improves segmentation speed,but also extracts effective features more ***,a contour initialization method for network learning was designed,and a global contour feature aggregation mechanism was used to return a coarse contour,which solves the problem of excessive error between manually initialized contour and real ***,the Snake deformation module was used to iteratively optimize the coarse contour to obtain the final instance *** experimental results showed that the proposed method improved the instance segmentation accuracy on semantic boundaries dataset(SBD),Cityscapes and Kins datasets,and the average precision reached 55.8 on the SBD;Compared with Deep Snake,the model parameters were reduced by 87.2%,calculation amount was reduced by 78.3%,and segmentation speed reached 39.8 frame·s−1 when instance segmentation was performed on an image with a size of 512×512 pixels on a 2080Ti *** proposed method can reduce resource consumption,realize instance segmentation tasks quickly and accurately,and therefore is more suitable for embedded platforms with limited resources.

关键词： instance segmentation ShuffleNet V2 lightweight network contour initialization

A multi-view K-multiple-means clustering method

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Journal of Measurement Science and Instrumentation 2021年第4期12卷 405-411页

作者： ZHANG Nini GE Hongwei School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi 214122 China

The K-multiple-means(KMM)retains the simple and efficient advantages of the K-means algorithm by setting multiple subclasses,and improves its effect on non-convex data *** aiming at the problem that it cannot be applied to the Internet on a multi-view data set,a multi-view K-multiple-means(MKMM)clustering method is proposed in this *** new algorithm introduces view weight parameter,reserves the design of setting multiple subclasses,makes the number of clusters as constraint and obtains clusters by solving optimization *** new algorithm is compared with some popular multi-view clustering *** effectiveness of the new algorithm is proved through the analysis of the experimental results.

关键词： K-multiple-means(KMM)clustering weight parameters multi-view K-multiple-means(MKMM)method

Multiplication fusion of sparse and collaborative-competitive representation for image classification

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arXiv 2020年

作者： Li, Zi-Qi Sun, Jun Wu, Xiao-Jun Yin, He-Feng School of Internet of Things Engineering Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China

Representation based classification methods have become a hot research topic during the past few years, and the two most prominent approaches are sparse representation based classification (SRC) and collaborative representation based classification (CRC). CRC reveals that it is the collaborative representation rather than the sparsity that makes SRC successful. Nevertheless, the dense representation of CRC may not be discriminative which will degrade its performance for classification tasks. To alleviate this problem to some extent, we propose a new method called sparse and collaborative-competitive representation based classification (SCCRC) for image classification. Firstly, the coefficients of the test sample are obtained by SRC and CCRC, respectively. Then the fused coefficient is derived by multiplying the coefficients of SRC and CCRC. Finally, the test sample is designated to the class that has the minimum residual. Experimental results on several benchmark databases demonstrate the efficacy of our proposed SCCRC. The source code of SCCRC is accessible at https://***/li-zi-qi/SCCRC. Copyright © 2020, The Authors. All rights reserved.

关键词： Image classification

MAAUNet:Exploration of U-shaped encoding and decoding structure for semantic segmentation of medical image

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Journal of Measurement Science and Instrumentation 2022年第4期13卷 418-429页

作者： SHAO Shuo GE Hongwei Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan UniversityWuxi 214122China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China

In view of the problems of multi-scale changes of segmentation targets,noise interference,rough segmentation results and slow training process faced by medical image semantic segmentation,a multi-scale residual aggregation U-shaped attention network structure of MAAUNet(MultiRes aggregation attention UNet)is proposed based on ***,aggregate connection is introduced from the original feature aggregation at the same *** connection is redesigned to aggregate features of different semantic scales at the decoder subnet,and the problem of semantic gaps is further solved that may exist between skip ***,after the multi-scale convolution module,a convolution block attention module is added to focus and integrate features in the two attention directions of channel and space to adaptively optimize the intermediate feature ***,the original convolution block is *** convolution channels are expanded with a series convolution structure to complement each other and extract richer spatial *** connections are retained and the convolution block is turned into a multi-channel convolution *** model is made to extract multi-scale spatial *** experimental results show that MAAUNet has strong competitiveness in challenging datasets,and shows good segmentation performance and stability in dealing with multi-scale input and noise interference.

关键词： U-shaped attention network structure of MAAUNet convolutional neural network encoding-decoding structure attention mechanism medical image semantic segmentation

Anomaly detection and segmentation based on multi-student teacher network

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Journal of Measurement Science and Instrumentation 2022年第2期13卷 235-241页

作者： REN Chaoqiang LIU Dengfeng School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi 214122 China

In automated industrial inspection,it is often necessary to train models on anomaly-free images and perform anomaly detection on products,which is also an important and challenging task in computer *** student-teacher network trains students to regress the output of the teacher,and uses the difference between the output of the student network and the pre-trained teacher network to locate anomalies,which has achieved advanced results in the field of abnormal ***,it is slow to predict a picture,and no anomaly detection is performed.A multi-student teacher network is proposed,which uses multiple student networks to jointly regress the output of the teacher network,and the minimum square difference between the output of students and teachers in each dimension is selected as the difference *** information in the middle layer of the network is used to represent each area of the image and calculate the anomaly distance for anomaly segmentation,and the maximum abnormal score is used to represent the abnormal degree of the image for abnormal *** results on MVTec anomaly detection show that the algorithm predicts a picture in 0.17 s and can output anomaly detection results at the same time,with image AUROC reaching 91.1%and Pixel AUROC reaching 94.5%.On the wall tile dataset produced by taking pictures of real scenes,image AUROC reached 89.7%,and Pixel AUROC reached 89.1%.Compared with the original student-teacher network,the proposed method can quickly complete anomaly segmentation and anomaly detection tasks at the same time with better accuracy,and it also has better results in real applications.

关键词： student-teacher network anomaly detection anomaly segmentation unsupervised learning

Adaptive multi-modal feature fusion for far and hard object detection

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Journal of Measurement Science and Instrumentation 2021年第2期12卷 232-241页

作者： LI Yang GE Hongwei Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi 214122 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China

In order to solve difficult detection of far and hard objects due to the sparseness and insufficient semantic information of LiDAR point cloud,a 3D object detection network with multi-modal data adaptive fusion is proposed,which makes use of multi-neighborhood information of voxel and image ***,design an improved ResNet that maintains the structure information of far and hard objects in low-resolution feature maps,which is more suitable for detection ***,semantema of each image feature map is enhanced by semantic information from all subsequent feature ***,extract multi-neighborhood context information with different receptive field sizes to make up for the defect of sparseness of point cloud which improves the ability of voxel features to represent the spatial structure and semantic information of ***,propose a multi-modal feature adaptive fusion strategy which uses learnable weights to express the contribution of different modal features to the detection task,and voxel attention further enhances the fused feature expression of effective target *** experimental results on the KITTI benchmark show that this method outperforms VoxelNet with remarkable margins,*** the AP by 8.78%and 5.49%on medium and hard difficulty ***,our method achieves greater detection performance compared with many mainstream multi-modal methods,*** the AP by 1%compared with that of MVX-Net on medium and hard difficulty levels.

关键词： 3D object detection adaptive fusion multi-modal data fusion attention mechanism multi-neighborhood features