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PHUI-GA: GPU-based efficiency evolutionary algorithm for mining high utility itemsets

Journal of Systems Engineering and Electronics 2024年第4期35卷 965-975页

作者： JIANG Haipeng WU Guoqing SUN Mengdan LI Feng SUN Yunfei FANG Wei Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Department of Computer Science and TechnologyJiangnan UniversityWuxi 214122China China Ship Scientific Research Center Wuxi 214082China Taihu Laboratory of Deepsea Technological Science Wuxi 214082China Department of Mathematics Nanjing UniversityNanjing 210023China

Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search *** have good running and mining performance,but they still require huge computational resource and may miss many *** to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded *** show that the mining performance of PHUI-GA outperforms the existing *** mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.

关键词： high utility itemset mining(HUIM) graphics process-ing unit(GPU)parallel genetic algorithm(GA) mining perfor-mance

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Rethinking the Quality of Synthetic Palm Vein Images from Spectral Analysis 27th

Rethinking the Quality of Synthetic Palm Vein Images from...

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27th Iberoamerican Congress on Progress in pattern recognition, Image Analysis, Computer Vision, and Applications, CIARP 2024

作者： Clarke, Colton Salazar-Jurado, Edwin H. Hernández-García, Ruber School of Financial Mathematics and Economics University of Victoria Victoria Canada Facultad de Ciencias Básicas Universidad Católica del Maule Maule Chile Departamento de Computación e Industrias Facultad de Ciencias de la Ingeniería Universidad Católica del Maule Maule Chile Laboratory of Technological Research on Pattern Recognition Universidad Católica del Maule Maule Chile

ISBN: (纸本)9783031766039

Palm vein-based biometric identification offers a higher level of security than traditional methods such as fingerprinting, iris, or facial recognition. One of its main advantages lies in using internal body features, which makes it highly secure and less susceptible to external changes. However, its large-scale application is limited by the need for large-scale public databases. In this context, synthetic palm vein image databases partially address this challenge, as there will always be a difference between synthetic and real. To mitigate these gaps, we propose to evaluate the differences using a spectral perspective and present techniques to fit the magnitude spectrum and power spectral distribution. We evaluated the similarity of the resulting synthetic images to the real images from the most representative state-of-the-art palm vein databases. The proposed approaches help to reduce the difference between synthetic and real images from the CASIA database, improving the accuracy in the representation of synthetic palm veins for the evaluation of biometric recognition algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Palmprint recognition

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Audio-Driven Dubbing for User Generated Contents via Style-Aware Semi-Parametric Synthesis

arXiv

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arXiv 2023年

作者： Song, Linsen Wu, Wayne Fu, Chaoyou Loy, Chen Change He, Ran The National Laboratory of Pattern Recognition CASIA Center for Research on Intelligent Perception and Computing CASIA Center for Excellence in Brain Science and Intelligence Technology CAS The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China SenseTime Research Beijing100080 China The S-Laboratory Nanyang Technological University Singapore639798 Singapore

Existing automated dubbing methods are usually designed for Professionally Generated Content (PGC) production, which requires massive training data and training time to learn a person-specific audio-video mapping. In this paper, we investigate an audio-driven dubbing method that is more feasible for User Generated Content (UGC) production. There are two unique challenges to design a method for UGC: 1) the appearances of speakers are diverse and arbitrary as the method needs to generalize across users;2) the available video data of one speaker are very limited. In order to tackle the above challenges, we first introduce a new Style Translation Network to integrate the speaking style of the target and the speaking content of the source via a cross-modal AdaIN module. It enables our model to quickly adapt to a new speaker. Then, we further develop a semi-parametric video renderer, which takes full advantage of the limited training data of the unseen speaker via a video-level retrieve-warp-refine pipeline. Finally, we propose a temporal regularization for the semi-parametric renderer, generating more continuous videos. Extensive experiments show that our method generates videos that accurately preserve various speaking styles, yet with considerably lower amount of training data and training time in comparison to existing methods. Besides, our method achieves a faster testing speed than most recent methods. © 2023, CC BY-NC-SA.

关键词： Rendering (computer graphics)

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Off-Line Writer Identification Based on Small Segments of Handwritten Text and Convolutional Neural Networks

SSRN

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SSRN 2022年

作者： Becerra, Guillermo Mora, Marco Aubin, Verónica Hernández-García, Ruber Laboratory of Technological Research in Pattern Recognition Universidad Católica del Maule Avenida San Miguel 3605 Talca3480112 Chile Department of Engineering and Technological Research Universidad Nacional de La Matanza Florencio Varela 1903 San JustoB1754JEC Argentina

This paper proposes a new method for writer identification based on small fragments of handwritten text randomly obtained from a paragraph. The main contribution of this work is to show that small fragments carry enough biometric information for writer identification. A second contribution is the creation of 2 repositories of images of handwritten text from 50 writers. The first one is made up of 4 text paragraphs of 64 words in high resolution per writer. The second one contains more than 700 thousand fragments of text per writer. Experiments were conducted with different Convolutional Neural Networks, considering the VGG-16, VGG19, InceptionV3, ResNet-50 and MobileNetV2m models. 2 classification schemes were implemented. First, the classification of individual fragments and, second, the classification of groups of fragments. The best results were obtained using groups of fragments, achieving accuracy of 96% on the identification of a text with the same semantic content and of 87% on the identification of the writer considering a text with different semantic content. © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

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TOWARDS THE GENERATION OF SYNTHETIC IMAGES OF PALM VEIN patternS: A REVIEW

arXiv

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arXiv 2022年

作者： Salazar-Jurado, Edwin H. Hernández-García, Ruber Vilches-Ponce, Karina Barrientos, Ricardo J. Mora, Marco Jaswal, Gaurav Doctorado en Modelamiento Matemático Aplicado Universidad Católica del Maule Talca3480112 Chile Laboratory of Technological Research in Pattern Recognition Research Center for Advanced Studies of Maule Universidad Católica del Maule Talca3480112 Chile Faculty of Basic Sciences Universidad Católica del Maule Talca3480112 Chile Laboratory of Technological Research in Pattern Recognition Faculty of Engineering Sciences Universidad Católica del Maule Talca3480112 Chile Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi110016 India

With the recent success of computer vision and deep learning, remarkable progress has been achieved on automatic personal recognition using vein biometrics. However, collecting large-scale real-world training data for palm vein recognition has turned out to be challenging, mainly due to the noise and irregular variations included at the time of acquisition. Meanwhile, existing palm vein recognition datasets are usually collected under near-infrared light, lacking detailed annotations on attributes (e.g., pose), so the influences of different attributes on vein recognition have been poorly investigated. Therefore, this paper examines the suitability of synthetic vein images generated to compensate for the urgent lack of publicly available large-scale datasets. Firstly, we present an overview of recent research progress of palm vein recognition, from the basic background knowledge to vein anatomical structure, data acquisition, public database, and quality assessment procedures. Then, we focus on the state-of-the-art methods that have allowed the generation of vascular structures for biometric purposes and the modeling of biological networks with their respective application domains. In addition, we review the existing research on the generation of style transfer and biological nature-based synthetic palm vein images algorithms. Afterward, we formalize a general flowchart for the creation of a synthetic database comparing real palm vein images and generated synthetic samples to obtain some understanding into the development of the realistic vein imaging system. Ultimately, we conclude by discussing the challenges, insights, and future perspectives in generating synthetic palm vein images for further works. Copyright © 2022, The Authors. All rights reserved.

关键词： Biometrics

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UniVIP: A Unified Framework for Self-Supervised Visual Pre-training

arXiv

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arXiv 2022年

作者： Li, Zhaowen Zhu, Yousong Yang, Fan Li, Wei Zhao, Chaoyang Chen, Yingying Chen, Zhiyang Xie, Jiahao Wu, Liwei Zhao, Rui Tang, Ming Wang, Jinqiao National Laboratory Of Pattern Recognition Institute Of Automation CAS Beijing China School Of Artificial Intelligence University Of Chinese Academy Of Sciences Beijing China SenseTime Research Development Research Institute Of Guangzhou Smart City Guangzhou China S-Lab Nanyang Technological University Singapore Peng Cheng Laboratory Shenzhen China Qing Yuan Research Institute Shanghai Jiao Tong University Shanghai China

Self-supervised learning (SSL) holds promise in leveraging large amounts of unlabeled data. However, the success of popular SSL methods has limited on single-centricobject images like those in ImageNet and ignores the correlation among the scene and instances, as well as the semantic difference of instances in the scene. To address the above problems, we propose a Unified Self-supervised Visual Pre-training (UniVIP), a novel self-supervised framework to learn versatile visual representations on either single-centric-object or non-iconic dataset. The framework takes into account the representation learning at three levels: 1) the similarity of scene-scene, 2) the correlation of scene-instance, 3) the discrimination of instanceinstance. During the learning, we adopt the optimal transport algorithm to automatically measure the discrimination of instances. Massive experiments show that Uni-VIP pre-trained on non-iconic COCO achieves state-of-the-art transfer performance on a variety of downstream tasks, such as image classification, semi-supervised learning, object detection and segmentation. Furthermore, our method can also exploit single-centric-object dataset such as ImageNet and outperforms BYOL by 2.5% with the same pre-training epochs in linear probing, and surpass current self-supervised object detection methods on COCO dataset, demonstrating its universality and potential. © 2022, CC BY.

关键词： Object recognition

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Multiplicative local binary patterns (MuLBP) 10

Multiplicative local binary patterns (MuLBP)

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10th International Conference on pattern recognition Systems, ICPRS 2019

作者： Mora, Marco Silva-Ibarra, Marcelo Acevedo-Letelier, Monica Department of Computer Science Universidad Católica Del Maule Chile Laboratory of Technological Research in Pattern Recognition Universidad Catolica Del Maule Chile

ISBN: (纸本)9781839531255

Speckle is a multiplicative noise that greatly deteriorates images. In this paper a model of Local Binary patterns (LBP) adapted to images with speckle (MuLBP) is proposed. The multiplicative model is constructed by substituting the additive comparisons of the traditional LBP for multiplicative comparisons from the Bigeometric Calculus. The experiments were carried out considering the 10.824 images of the KTH-TIPS2, FMD, CASIA and UFI databases. To compare the additive and multiplicative models, the Euclidean distance between the LBP histograms of the image with noise and without noise is adopted. The results indicate that, the distance between the histograms, the image with noise with respect to the image without noise, is smaller for the multiplicative models than for the traditional additive models. The above means that, the multiplicative LBP represent in a better way the textures in images contaminated with speckle. © 2019 IET Conference Publications. All rights reserved.

关键词： Graphic methods

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 IEEE/CVF Conference on Computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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Contour detection in images with speckle by using the bi-geometric structure 4

Contour detection in images with speckle by using the bi-geo...

引用

4th International Conference/Days of Applied Mathematics

作者： Acevedo-Letelier, M. Vilches, K. Mora, M. Departamento de Matemática Fisica y Estadistica Universidad Católica Del Maule Talca Chile Laboratory of Technological Research in Pattern Recognition Universidad Católica Del Maule Talca Chile

The speckle produces severe degradations in digital images, which could be introduced by technological devices during the acquisition, capture or transmission of the images. This perturbation affects, for instance, medical and satellite images. Recently, some methods defined in the bi-geometric structure have presented significant improvements in the processing of image with speckle. In this work, we review some aspects of the bi-geometric structure including a discussion about some of the applications found in the literature focused in the detection of contours in grey scale images with speckle. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Speckle

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Multiplicative Local Binary patterns (MuLBP)

Multiplicative Local Binary Patterns (MuLBP)

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International Conference on pattern recognition Systems

作者： Marco Mora Marcelo Silva-Ibarra Monica Acevedo-Letelier Department of Computer Science Universidad Católica del Maule Chile Laboratory of Technological Research in Pattern Recognition Universidad Católica del Maule Chile Laboratory of Technological Research in Pattern Recognition Universidad Católica del Maule Chile

ISBN: (数字)9781839531088

关键词： Local binary patterns Speckle Bi-geometric calculus

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