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A data representation method using distance correlation

Frontiers of Computer Science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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Adaptive Method of Process Flow Parameter Values Setting Based on Remote Sensing Image Metadata

Adaptive Method of Process Flow Parameter Values Setting Bas...

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2022 International Conference on Signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Zhou, Bing Ge, Jian Kang Zuo, Xianyu Key Laboratory of Analysis and Processing of Big Data Henan Kaifeng475004 China School of Computer and Information Engineering Henan University Henan Kaifeng475004 China

ISBN: (纸本)9781510664616

Remote sensing data processing involves many steps. This paper uses process flow to represent these processing steps. This paper combines remote sensing image metadata (RSIM) with case based reasoning (CBR) technology, proposes a parameter value adaptive method based on RSIM, and constructs a remote RSIM case model. It makes the representation of RSIM more intuitive and convenient. This method consists of two parts: case building and case similarity measurement. Case building refers to building RSIM into RSIM cases according to case models, and then classifying and saving each case. Case similarity measurement refers to the similarity measurement of metadata in the production process and each case under the flow. Finally, the parameter values will be configured according to the highest similarity. The method is tested in an actual remote sensing production system, and the experimental results show that the method has good applicability in the parameter value adaptation of remote sensing process production. © 2023 SPIE.

关键词： Remote sensing

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Anima2: Cross-Species Animal Animation through Image-to-Video Synthesis with Subject Alignment

Anima2: Cross-Species Animal Animation through Image-to-Vide...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Xu, Yuanfeng Chen, Yuhao Huang, Zhongzhan He, Zijian Wang, Guangrun Lin, Liang Sun Yat-sen University China Peng Cheng Laboratory China Guangdong Key Laboratory of Big Data Analysis and Processing China

ISBN: (纸本)9798350368741

Recent video editing advancements rely on accurate pose sequences to animate human actors. However, these efforts are not suitable for cross-species animation due to pose misalignment between species (for example, the poses of a cat differ greatly from that of a pig due to their distinct body structures). In this paper, we present Anima2, a zero-shot diffusion-based video generator to address this issue, aiming to accurately ANIMAte ANIMAls while preserving the background. The key technique involves two-fold subject alignment. First, we improve appearance feature extraction by integrating a Laplacian detail booster and a prompt-tuning identity extractor. They capture essential appearance information, including identity and fine details. Second, we align shape features and address conflicts from differing animals by introducing a scale-information remover and an adaptive rescaling module. They both enhance subject alignment for accurate cross-species animation. Additionally, we introduce two high-quality animal video datasets with diverse species to benchmark cross-species animation. Trained on these extensive datasets, our model directly generates videos with accurate movements, consistent appearances, and high-fidelity frames, eliminating the need for test-time training. Extensive experiments demonstrate our method's superiority in cross-species animation, showcasing robust adaptability and generality. © 2025 IEEE.

关键词： cross-species subject alignment video editing

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A new double scrambling color image encryption algorithm based on multi chaotic systems and image segmentation 3

A new double scrambling color image encryption algorithm bas...

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3rd International Conference on Computer Science and Management Technology, ICCSMT 2022

作者： Tian, Junfeng Wang, Ke Liu, Cheng Henan University Key Laboratory of Big Data Analysis and Processing of Henan Province Kaifeng China

ISBN: (纸本)9798350320404

Aiming at low security of traditional encryption methods, this paper give a new encryption method by using multi chaotic systems and image segmentation. Firstly, use Arnold transform to perturb the RGB component matrices of the source image. Subsequently, the chaotic image chunking algorithm is applied to the mixed component matrix, and the chunked mixed matrix is perturbed by Hyperchaotic Chen system and Logistic mapping. Moreover, Diffusion of pixel transformations to the whole image by inverse transform operations. Finally, logistic system is applied to the secondary pixels scrambling, which achieves image encryption. The experimental data indicate that this encryption method encryption effect is good, and can effectively prevent ciphertext attack and noise attack. © 2022 IEEE.

关键词： Image segmentation

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LLDB: Efficient Low-Light Image Enhancement with Difffusion Bridge

LLDB: Efficient Low-Light Image Enhancement with Difffusion ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Ma, Junlong Yue, Conghan Peng, Zhengwei Zhang, Dongyu Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9798350368741

This paper investigates a low-light image enhancement method based on the diffusion bridge framework. Currently, low-light image enhancement tasks still face challenges in noise reduction and detail restoration, and existing diffusion model methods are time-consuming and have unstable diffusion processes. We conducted an in-depth study of diffusion bridge theory, integrating the advantages of the end-to-end paradigm of diffusion bridge theory and employing a nonlinear activation network to further enhance the performance of low-light image enhancement tasks. Additionally, we used a Gamma correction module for fine-tuning low-light images, significantly improving performance with almost no extra computational cost. Experiments show that Low-Light image enhancement with Difffusion Bridge (LLDB) far surpasses other methods on LOLv1 and LOLv2 datasets. Code is available at https://***/M-Chase/LLDB. © 2025 IEEE.

关键词： Diffusion Bridge Diffusion Model Low-Light image Enhancement

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A MAB-based Hyper-heuristic for Vehicle Routing Problem with Time Window

A MAB-based Hyper-heuristic for Vehicle Routing Problem with...

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2022 International Conference on Signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Hou, Yan-E Liu, Bingbing Fan, Gaojuan Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China

ISBN: (纸本)9781510664616

In order to efficiently solve the vehicle routing problem with time window (VRPTW), a hyper-heuristic algorithm based on reinforcement learning was proposed. Firstly, the performance of the underlying heuristic algorithm was evaluated, and then a multi-armed bandit (MAB) algorithm was used to select the low-level heuristic algorithm. At the same time, a simulated annealing-based acceptance rule was used to determine whether to accept the solution obtained by each low-level heuristic to ensure the diversity of solutions. Experiments was carried out on some VRPTW benchmark instances, and the results show that the proposed algorithm is effective and stable. © 2023 SPIE.

关键词： Simulated annealing

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Application of remote sensing based on algorithm process technology inspection processing method

Application of remote sensing based on algorithm process tec...

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2022 International Conference on Signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Ge, Qiang Jiang, Kejian Han, Hongyu Liu, Huaizhou Zhang, Jing Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475001 China School of Computer and Information Engineering Henan University Kaifeng475001 China

ISBN: (纸本)9781510664616

Aiming at a series of problems such as low evaluation efficiency of traditional algorithms, cumbersome process operation, high use cost and no complete algorithm inspection process system, this paper proposes an algorithm flow technology inspection processing system and method based on remote sensing applications, mainly including satellite product processing, algorithm processing, sample feature selection, sample extraction, and algorithm processing results comparison, It mainly realizes the functions of input, output and parameter adjustment required by different function functions, and designs an interface interaction system that adapts to the input and output elements required by different function modules. It realizes the modular, plug-in and componentized design of interface interaction of function modules, and obtains the evaluation system of algorithm credibility. © 2023 SPIE.

关键词： Inspection

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SymGaussian: Occluded Human Rendering with Multi-scale Symmetry Feature from Monocular Video

SymGaussian: Occluded Human Rendering with Multi-scale Symme...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Jiang, Zekai Duan, Tong Zhang, Dongyu Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9798350368741

The growing demand for high-quality 3D human rendering in real-world applications highlights significant challenges. These challenges are particularly evident in dealing with occlusion in monocular video. Previous methods often rely on controlled datasets and overlook the inherent symmetry of the human body, leading to incomplete rendering in occluded areas. To address these limitations, we propose SymGaussian, a novel Gaussian Splatting-based approach for rendering occluded human from monocular video. We introduce Multi-scale Symmetry Feature to compensate for lost information in occluded areas, along with a Projective Texture Mapping method that efficiently encodes 2D appearance while preserving 3D perception. Experiments show that SymGaussian outperforms state-of-the-art methods in rendering quality, while achieving rapid training speed and real-time rendering capability exceeding 200 FPS. © 2025 IEEE.

关键词： 3D Gaussian Splatting Multi-scale Symmetry Feature Occluded Human Rendering Texture Mapping

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An effective global learning framework for hyperspectral image classification based on encoder-decoder architecture

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International Journal of Digital Earth 2022年第1期15卷 1350-1376页

作者： Lanxue Dang Chongyang Liu Weichuan Dong Yane Hou Qiang Ge Yang Liu Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifengChina School of Computer and Information Engineering Henan UniversityKaifengChina Department of Geography Kent State UniversityKentOHUSA

Most deep learning methods in hyperspectral image(HSI)classification use local learning methods,where overlapping areas between pixels can lead to spatial redundancy and higher computational *** paper proposes an efficient global learning(EGL)framework for HSI *** EGL framework was composed of universal global random stratification(UGSS)sampling strategy and a classification model *** UGSS sampling strategy was used to solve the problem of insufficient gradient variance resulted from limited training *** fully extract and explore the most distinguishing feature representation,we used the modified linear bottleneck structure with spectral attention as a part of the BrsNet network to extract spectral spatial *** a type of spectral attention,the shuffle spectral attention module screened important spectral features from the rich spectral information of HSI to improve the classification accuracy of the ***,we also designed a double branch structure in BrsNet that extracted more abundant spatial information from local and global perspectives to increase the performance of our classification *** were conducted on three famous datasets,IP,PU,and *** with other classification methods,our proposed method produced competitive results in training time,while having a greater advantage in test time.

关键词： Deep learning global learning feature representation hyperspectral image classification spectral attention

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Multimodal Identity Recognition of Spiking Neural Network Based on Brain-Inspired Reward Propagation 22

Multimodal Identity Recognition of Spiking Neural Network Ba...

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22nd IEEE International Symposium on Parallel and Distributed processing with Applications, ISPA 2024

作者： Liu, Yang Zhang, Hongxia Li, Yahui Wang, Quanqiang Liu, Cheng Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan Kaifeng475004 China Henan University School of Computer and Information Engineering Henan Kaifeng475004 China Huaihe Hospital of Henan University Department of Clinical Laboratory Henan Kaifeng475004 China

ISBN: (纸本)9798331509712

Deep learning has strongly promoted the research progress of unimodal feature learning such as face recognition and speech recognition, but it still needs to be strengthened in multimodal data representation learning, especially the problem of high power consumption, which is not conducive to the real-time application of mobile devices under limited resources. In order to realize multimodal identity recognition for low-energy edge smart devices, this paper proposes a multimodal identity recognition method of spiking neural network based on brain-like mechanism (SNN-MIR-BiM). Simulating the attention mechanism of the cognitive system to specific information improves the response of multimodal features, mimics the regulation mechanism of the neurotransmitter dopamine in the nervous system, and releases reward signals from the recognition results, which affects the neuronal state and regulates the synaptic weights through global propagation, and then optimizes the network structure. Experimental results on large-scale audio and video datasets show that the proposed method can reach 98.94% recognition accuracy, and the energy consumption of the model is about 1/10 of that of CNN under the same architecture, which verifies the effectiveness of the attention mechanism and reward propagation in network design. This research is of great practical significance in the industry applications of unmanned autonomous systems such as human-computer interaction of intelligent machines and intelligent security. © 2024 IEEE.

关键词： Face recognition

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