检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Yin, Zhenyu Yang, Xibei Wang, Pingxin Yu, Hualong Qian, Yuhua School of Computer Jiangsu University of Science and Technology Jiangsu Zhenjiang212100 China School of Science Jiangsu University of Science and Technology Jiangsu Zhenjiang212100 China School of Economics and Management Jiangsu University of Science and Technology Jiangsu Zhenjiang212100 China Institute of Big Data Science and Industry Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China

Among various tasks of feature reduction, how to search qualified features and then construct the corresponding reduct which satisfies a given constraint is a hot topic. Constraint is the pre-defined condition in feature reduction, it rules two crucial aspects: 1) when the process of searching can be terminated to construct reduct;2) whether a feature/set of features should be added into reduct pool or not. Obviously, such a task of feature reduction is constraint-dependent. However, some limitations may naturally emerge: 1) constraint-independent features are ignored;2) fixed constraint hinders the diverse evaluations of features;3) selected features based on single constraint are powerless to data perturbation. Therefore, an Ensembler Mixed with Pareto Optimality (EmPo) is developed in this study. Firstly, the principle of Restricted Pareto Optimality is proposed to identify constraint-independent features before performing constraint related searching, which indicates a two-stages strategy to feature reduction. Naturally, diverse evaluations of features are achieved in such a process. Secondly, a data perturbation w.r.t. either sample or feature aspect is employed to obtain multiple reductions of features. The objective is to further improve the stability of the classification results based on those multiple reducts. It should also be emphasized that EmPo is a general framework, and most existing approaches to feature reduction can be embedded into it to further improve their performances. Finally, the effectiveness of our EmPo is validated over 20 datasets within 4 different ratios of noisy label. © 2023, The Authors. All rights reserved.

关键词： Pareto principle

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Novel contour-feature retrieval algorithm

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Guangdianzi Jiguang/Journal of Optoelectronics Laser 2009年第1期20卷 108-112页

作者： Sun, Jun-Ding Li, Quan-Xi Wu, Xiao-Sheng School of Computer Science and Technology Henan Polytechnic University Jiaozuo 454003 China Provincial Key Laboratory of Computer Information Processing Technology Suzhou University Suzhou 215006 China

The paper presents a novel approach for contour-based shape retrieval. The contour is firstly sampled and smoothed by a Gaussian filter. Then, the sampled points are classified into salient (convex or concave) or smooth type points by their internal angles. In addition to distance histograms (DH), two new descriptors named relative address distribution (RAD) and relative unit entropy (RUE) are introduced to describe the correlation of each type of contour points. These descriptors have powerful descriptive ability for contour with more spatial information. Comparisons are conducted between the proposed method and several other feature descriptors. The results show that the new method is efficient and it provides noticeable improvement to the performance of shape retrieval.

关键词： Entropy

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Grafting Transformer on Automatically Designed Convolutional Neural Network for Hyperspectral Image Classification

arXiv

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arXiv 2021年

作者： Xue, Xizhe Zhang, Haokui Fang, Bei Bai, Zongwen Li, Ying The School of Computer Science National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xi’an China Harbin Institute of Technology Shenzhen China The Key Laboratory of Modern Teaching Technology Ministry of Education Shaanxi Normal University Xi’an China The School of Physics and Electronic Information Yan'an University Yan’an China

Hyperspectral image (HSI) classification has been a hot topic for decides, as hyperspectral images have rich spatial and spectral information and provide strong basis for distinguishing different land-cover objects. Benefiting from the development of deep learning technologies, deep learning based HSI classification methods have achieved promising performance. Recently, several neural architecture search (NAS) algorithms have been proposed for HSI classification, which further improve the accuracy of HSI classification to a new level. In this paper, NAS and Transformer are combined for handling HSI classification task for the first time. Compared with previous work, the proposed method has two main differences. First, we revisit the search spaces designed in previous HSI classification NAS methods and propose a novel hybrid search space, consisting of the space dominated cell and the spectrum dominated cell. Compared with search spaces proposed in previous works, the proposed hybrid search space is more aligned with the characteristic of HSI data, that is, HSIs have a relatively low spatial resolution and an extremely high spectral resolution. Second, to further improve the classification accuracy, we attempt to graft the emerging transformer module on the automatically designed convolutional neural network (CNN) to add global information to local region focused features learned by CNN. Experimental results on three public HSI datasets show that the proposed method achieves much better performance than comparison approaches, including manually designed network and NAS based HSI classification methods. Especially on the most recently captured dataset Houston University, overall accuracy is improved by nearly 6 percentage points. Code is available at: https://***/Cecilia-xue/HyT-NAS. © 2021, CC BY.

关键词： Convolutional neural networks

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Decision-based iterative fragile watermarking for model integrity verification

arXiv

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arXiv 2023年

作者： Yin, Zhaoxia Yin, Heng Su, Hang Zhang, Xinpeng Gao, Zhenzhe Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai Shanghai200241 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University Anhui Hefei230000 China Department of Computer Science and Technology Tsinghua University Beijing Beijing100000 China School of Computer Science and Technology Fudan University Shanghai Shanghai200241 China

With the development of artificial intelligence, fine-tuning pretrained models has become increasingly accessible for average users, enabling them to easily adapt models to their specific tasks. As a result, the practical applications of such models have been further accelerated across various fields. Typically, foundation models are hosted on cloud servers to meet the high demand for their services. However, this exposes them to security risks, as attackers can modify them after uploading to the cloud or transferring from a local system. To address this issue, we propose an iterative decision-based fragile watermarking algorithm that transforms normal training samples into fragile samples that are sensitive to model changes. We then compare the output of sensitive samples from the original model to that of the compromised model during validation to assess the model’s completeness. The proposed fragile watermarking algorithm is an optimization problem that aims to minimize the variance of the predicted probability distribution outputed by the target model when fed with the converted sample. We convert normal samples to fragile samples through multiple iterations. Our method has some advantages: (1) the iterative update of samples is done in a decision-based black-box manner, relying solely on the predicted probability distribution of the target model, which reduces the risk of exposure to adversarial attacks, (2) the small-amplitude multiple iterations approach allows the fragile samples to perform well visually, with a PSNR of 55 dB in TinyImageNet compared to the original samples, (3) even with changes in the overall parameters of the model of magnitude 1e-4, the fragile samples can detect such changes, and (4) the method is independent of the specific model structure and dataset. We demonstrate the effectiveness of our method on multiple models and datasets, and show that it outperforms the current state-of-the-art. Copyright © 2023, The Authors. All rights reser

关键词： Probability distributions

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Hybrid discrimination method for samples classification in medicine

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International Journal of Simulation: Systems, Science and technology 2016年第27期17卷 17.1-17.9页

作者： Chen, Xiaoyu Liu, Bo Xia, Xin Yan, Dandan Yan, Wang Ma, Lizhuang Department of Information Center East Hospital Tongji University School of Medicine Jimo Road 150 Shanghai China School of Continuing Education Shanghai Jiao Tong University Shanghai200240 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China Department of Computer Science Shanghai Jiao Tong University Shanghai200240 China

As indispensable solutions in classification problems, discrimination for samples has been employed in medicine, and it is performed subjectively by physicians at present, which hinders the diagnosis and treatment in medicine. In this paper, a hybrid discrimination method (HDM) in medicine is proposed, which consists of two phases, including attribute selection phase and discriminant phase. In attribute selection phase, critical attributes are selected from the original features by linear correlation and C5.0 decision tree. In discriminant phase, samples are discriminated by discriminant analysis. This discrimination method is evaluated through five datasets of chronic hepatitis B, cardiac Single Proton Emission Computed Tomography (SPECT) images, Lung Cancer, Hepatitis survival and Iris plant for demonstrating its viability and applications. Finally, this proposed method has obtained the critical clinical lab indicators and discriminants related to three syndromes in CHB dataset, and it also performs well than some typical classification methods in the other four datasets for its broader applications. © 2016, UK Simulation Society. All rights reserved.

关键词： Discriminant analysis

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EWT: Efficient Wavelet-Transformer for Single Image Denoising

arXiv

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arXiv 2023年

作者： Li, Juncheng Cheng, Bodong Chen, Ying Gao, Guangwei Zeng, Tieyong The School of Communication & Information Engineering Shanghai University Shanghai China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China The School of Computer Science and Technology Xidian University Xian China The Department of Cyberspace Security Beijing Electronic Science & Technology Institute Beijing China The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China The Department of Mathematics The Chinese University of Hong Kong New Territories Hong Kong

Transformer-based image denoising methods have achieved encouraging results in the past year. However, it must uses linear operations to model long-range dependencies, which greatly increases model inference time and consumes GPU storage space. Compared with convolutional neural network-based methods, current Transformer-based image denoising methods cannot achieve a balance between performance improvement and resource consumption. In this paper, we propose an Efficient Wavelet Transformer (EWT) for image denoising. Specifically, we use Discrete Wavelet Transform (DWT) and Inverse Wavelet Transform (IWT) for downsampling and upsampling, respectively. This method can fully preserve the image features while reducing the image resolution, thereby greatly reducing the device resource consumption of the Transformer model. Furthermore, we propose a novel Dual-stream Feature Extraction Block (DFEB) to extract image features at different levels, which can further reduce model inference time and GPU memory usage. Experiments show that our method speeds up the original Transformer by more than 80%, reduces GPU memory usage by more than 60%, and achieves excellent denoising results. All code will be public. © 2023, CC BY.

关键词： Image denoising

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Discriminative feature combination selection for enhancing multiclass classification

Discriminative feature combination selection for enhancing m...

引用

International Conference on Behavior, Economic and Social Computing (BESC)

作者： Aibo Song Wei Qian Zhiang Wu Jinghua Zhao School of Computer Science and Engineering Southeast University Nanjing China Jiangsu Provincial Key Laboratory of E-Business Nanjing University of Finance and Economics Nanjing China College of Information Science and Engineering Shandong University of Science and Technology Shandong China

Frequent pattern mining is commonly utilized to generate combined-feature candidates, yet many are non-discriminative and thus might be useless for predictive models. In this paper, we propose to use feature combinations derived from frequent patterns to obtain more accurate multiclass classification models. Specifically, we present a novel mathematics inference to show what are discriminative feature combinations. Hence, an efficient algorithm is proposed for mining and selecting discriminative patterns. Experimental results on twenty UCI datasets demonstrate that the proposed method can help to improve the classification performance remarkably, compared with other baseline methods. Moreover, an internal evaluation is employed to validate the strong discriminative power of our feature combinations.

关键词： Breast Diabetes Vehicles Classification algorithms Niobium Size measurement

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Disentangled Generation Network for Enlarged License Plate Recognition and A Unified Dataset

arXiv

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arXiv 2022年

作者： Li, Chenglong Yang, Xiaobin Wang, Guohao Zheng, Aihua Tan, Chang Jia, Ruoran Tang, Jin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China iFLYTEK Co. Ltd. Hefei230088 China

License plate recognition plays a critical role in many practical applications, but license plates of large vehicles are difficult to be recognized due to the factors of low resolution, contamination, low illumination, and occlusion, to name a few. To overcome the above factors, the transportation management department generally introduces the enlarged license plate behind the rear of a vehicle. However, enlarged license plates have high diversity as they are non-standard in position, size, and style. Furthermore, the background regions contain a variety of noisy information which greatly disturbs the recognition of license plate characters. Existing works have not studied this challenging problem. In this work, we first address the enlarged license plate recognition problem and contribute a dataset containing 9342 images, which cover most of the challenges of real scenes. However, the created data are still insufficient to train deep methods of enlarged license plate recognition, and building large-scale training data is very time-consuming and high labor cost. To handle this problem, we propose a novel task-level disentanglement generation framework based on the Disentangled Generation Network (DGNet), which disentangles the generation into the text generation and background generation in an end-to-end manner to effectively ensure diversity and integrity, for robust enlarged license plate recognition. Extensive experiments on the created dataset are conducted, and we demonstrate the effectiveness of the proposed approach in three representative text recognition frameworks. © 2022, CC BY.

关键词： Optical character recognition

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HSR-Diff: Hyperspectral Image Super-Resolution via Conditional Diffusion Models

HSR-Diff: Hyperspectral Image Super-Resolution via Condition...

引用

International Conference on computer Vision (ICCV)

作者： Chanyue Wu Dong Wang Yunpeng Bai Hanyu Mao Ying Li Qiang Shen National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing School of Computer Science Northwestern Polytechnical University China Department of Computer Science Faculty of Business and Physical Sciences Aberystwyth University U.K.

Despite the proven significance of hyperspectral images (HSIs) in performing various computer vision tasks, its potential is adversely affected by the low-resolution (LR) property in the spatial domain, resulting from multiple physical factors. Inspired by recent advancements in deep generative models, we propose an HSI Super-resolution (SR) approach with Conditional Diffusion Models (HSR-Diff) that merges a high-resolution (HR) multispectral image (MSI) with the corresponding LR-HSI. HSR-Diff generates an HR-HSI via repeated refinement, in which the HR-HSI is initialized with pure Gaussian noise and iteratively refined. At each iteration, the noise is removed with a Conditional Denoising Transformer (CDFormer) that is trained on denoising at different noise levels, conditioned on the hierarchical feature maps of HR-MSI and LR-HSI. In addition, a progressive learning strategy is employed to exploit the global information of full-resolution images. Systematic experiments have been conducted on four public datasets, demonstrating that HSR-Diff outperforms state-of-the-art methods.

关键词：

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DuDo-RAC: Dual-Domain Optimization for Ring Artifact Correction in Photon Counting Ct

SSRN

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SSRN 2024年

作者： Kan, Shengqi Ren, Chenlong Liu, Ze Lu, Yuchen Luo, Shouhua Ji, Xu Chen, Yang Laboratory of Image Science and Technology School of Computer Science and Engneering Southeast University Nanjing210096 China Laboratory of Biomedical Engineering College of Biological Science and Medical Engineering Southeast University Nanjing210096 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Nanjing210096 China

Background and objective: Due to the inconsistent response of photon counting detectors (PCDs) pixels to X-rays, there is a significant presence of low-frequency ring artifacts in CT reconstructed images. Traditional CT ring artifact correction methods are ineffective in correcting low-frequency ring artifacts. Although pixel-wise correction methods can correct low-frequency ring artifacts, there may still remain residual artifacts due to the inaccuracy in the coefficients measurement and the inability of polynomial functions to perfectly model the relationship between the thickness and post-log raw data. To resolve such the problems, this work proposes a high and low frequency ring artifact correction method based on dual-domain optimization (DuDo-RAC). Methods: This method does not require spectral information or training data and can be applicable for multiple energy thresholds. Its principle is to model the inconsistent response as pixel-wise polynomial functions. The pixel-specific polynomial coefficients are determined through a dual-domain optimization framework. Since ring artifacts manifest as stripes after polar transforms, smoothing operations are utilized to further weaken the residual ring artifacts after the pre-correction process. Furthermore, a multi-resolution gradient loss function is designed to continuously optimize the polynomial correction coefficients for better measuring the performance of ring removal. Results: The results have demonstrated that the proposed method can effectively correct the high and low frequency ring artifacts in PCD-CT images while preserving the image structures and ***: DuDo-RAC proposed in this study obtains impressive results in PCD-CT images, demonstrating the application potential. © 2024, The Authors. All rights reserved.

关键词： Photons

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