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Exploring data sampling techniques for imbalanced classification problems 探索不平衡分类问题的数据采样技术
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540457
Yu Sui, Xiao-hui Zhang, Jia-jia Huan, Hai-feng Hong
{"title":"Exploring data sampling techniques for imbalanced classification problems","authors":"Yu Sui, Xiao-hui Zhang, Jia-jia Huan, Hai-feng Hong","doi":"10.1117/12.2540457","DOIUrl":"https://doi.org/10.1117/12.2540457","url":null,"abstract":"The class imbalance problem is one of the key challenges in machine learning and data mining. Imbalanced data can result in the sub-optimal performance of classification models. To address the problem, a variety of data sampling methods have been proposed in previous studies. However, there is no universal solution and it is worth to explore which kind of data sampling technique is more effective in balancing class distribution in terms of the type of data and classifier. In this work, we present an experimental study based on a number of real-world data sets obtained from different disciplines. The goal is to investigate different sampling techniques in terms of the effectiveness of increasing the classification performance in imbalanced data sets. In particular, we study ten sampling methods of different types, including random sampling, clusterbased sampling, ensemble sampling and so on. Besides, the C4.5 decision tree algorithm is used to train the base classifiers and the performance is measured by using precision, G-Measure and Cohen's Kappa statistic.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"104 1","pages":"1119813 - 1119813-5"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87570944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Unsupervised classification of PolSAR image based on tensor product graph diffusion 基于张量积图扩散的PolSAR图像无监督分类
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540397
Meilin Li, H. Zou, Qian Ma, Jiachi Sun, Xu Cao, Xianxiang Qin
{"title":"Unsupervised classification of PolSAR image based on tensor product graph diffusion","authors":"Meilin Li, H. Zou, Qian Ma, Jiachi Sun, Xu Cao, Xianxiang Qin","doi":"10.1117/12.2540397","DOIUrl":"https://doi.org/10.1117/12.2540397","url":null,"abstract":"This paper presents a new unsupervised classification framework based on tensor product graph (TPG) diffusion, which is generally utilized for optical image segmentation or image retrieval and for the first time used for PolSAR image classification in our work. First, the PolSAR image is divided into many superpixels by using a fast superpixel segmentation method. Second, seven features are extracted from the PolSAR image to form a feature vector based on segmented superpixels and construct a similarity matrix by using the Gaussian kernel. Third, TPG diffusion is performed on this similarity matrix to obtain a more discriminative similarity matrix by mining the higher order information between data points. Finally, spectral clustering based on diffused similarity matrix is adopted to automatically achieve the classification results. The experimental results conducted on both a simulated PolSAR image and a real-world PolSAR image demonstrate that our algorithm can effectively combine higher order neighborhood information and achieve higher classification accuracy.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"52 1","pages":"111980C - 111980C-6"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86776249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Group binary weight networks 群二元权重网络
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540888
K. Guo, Yicai Yang, Xiaofen Xing, Xiangmin Xu
{"title":"Group binary weight networks","authors":"K. Guo, Yicai Yang, Xiaofen Xing, Xiangmin Xu","doi":"10.1117/12.2540888","DOIUrl":"https://doi.org/10.1117/12.2540888","url":null,"abstract":"In recent years, quantizing the weights of a deep neural network draws increasing attention in the area of network compression. An efficient and popular way to quantize the weight parameters is to replace a filter with the product of binary values and a real-valued scaling factor. However, the quantization error of such binarization method raises as the number of a filter's parameter increases. To reduce quantization error in existing network binarization methods, we propose group binary weight networks (GBWN), which divides the channels of each filter into groups and every channel in the same group shares the same scaling factor. We binarize the popular network architectures VGG, ResNet and DesneNet, and verify the performance on CIFAR10, CIFAR100, Fashion-MNIST, SVHN and ImageNet datasets. Experiment results show that GBWN achieves considerable accuracy increment compared to recent network binarization methods, including BinaryConnect, Binary Weight Networks and Stochastic Quantization Binary Weight Networks.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"12 1","pages":"1119812 - 1119812-6"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88563267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multi-parameter geometric measurement of piston based on laser projection 基于激光投影的活塞多参数几何测量
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540374
Liu Sen, Cheng Wei, Qingzeng Ma, Xinqiang Ma, H. Ge
{"title":"Multi-parameter geometric measurement of piston based on laser projection","authors":"Liu Sen, Cheng Wei, Qingzeng Ma, Xinqiang Ma, H. Ge","doi":"10.1117/12.2540374","DOIUrl":"https://doi.org/10.1117/12.2540374","url":null,"abstract":"Aiming at the problem of low accuracy and poor safety of traditional piston contact detection, a multi-parameter geometric measurement system of piston based on laser projection is proposed in this paper. Based on the principle of parallel light column projection imaging, a mechanical structure, such as motion control based on board card, was designed to realize multi-parameter non-contact detection of piston ring groove width, ring groove depth and ring groove inclination. Through the experimental study, the size tolerance of piston ring groove is about 0.02 mm, and the angle tolerance is about 0.2 degree, also the error analysis of the whole measuring system is carried out. The results show that it is feasible to detect the multi-parameter geometry of piston ring groove by laser projection principle, and it can simplify the step of piston detection, ensure the detection accuracy and improve the efficiency.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"70 1","pages":"111980X - 111980X-5"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79964996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Recaptured image detection based on convolutional neural networks with local binary patterns coding 基于局部二值模式编码的卷积神经网络图像检测
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540496
Nan Zhu, Minying Qin, Yuting Yin
{"title":"Recaptured image detection based on convolutional neural networks with local binary patterns coding","authors":"Nan Zhu, Minying Qin, Yuting Yin","doi":"10.1117/12.2540496","DOIUrl":"https://doi.org/10.1117/12.2540496","url":null,"abstract":"With the great development of image display technology and the widespread use of various image acquisition device, recapturing high-quality images from high-fidelity LCD (liquid crystal display) screens becomes relative convenient. These recaptured images pose serious threats on image forensic technologies and bio-authentication systems. In order to prevent the security loophole of image recapture attack, inspired by the effectiveness of LBP (local binary pattern) on recaptured image detection and the satisfactory performance of deep learning techniques on many image forensics tasks, we propose a recaptured image detection method based on convolutional neural networks with local binary patterns coding. The LBP coded maps are extracted as the input of the proposed convolutional neural networks architecture. Extensive experiments on two public high-quality recaptured image databases under two different scenarios demonstrate the superior of our designed method when compared with the state-of-the-art approaches.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"20 1","pages":"1119804 - 1119804-6"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88360704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Roaming of oblique photography model in unity3D unity3D中倾斜摄影模型的漫游
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540424
Guangyue Wang, Yang Peng, Pengfei Zhang, Maojun Zhang
{"title":"Roaming of oblique photography model in unity3D","authors":"Guangyue Wang, Yang Peng, Pengfei Zhang, Maojun Zhang","doi":"10.1117/12.2540424","DOIUrl":"https://doi.org/10.1117/12.2540424","url":null,"abstract":"The size of 3D model reconstructed based on oblique photography is always too large to load into Unity3D efficiently and robustly for roaming. To solve this problem, we propose a novel roaming method for oblique photography threedimensional models in Unity3D. The method can quickly load large-scale oblique photography model in Unity3D and realize fluency virtual roaming. Firstly, different level of detail models are generated by using LOD (level of detail) technology and divide the LOD models into blocks with same size. Secondly, we load the entire low LOD model as a panoramic view of the scene and load little high LOD model blocks around the location of viewpoint dynamically while roaming. A Nine-palace mode is adopted for high LOD model blocks selection strategy. Finally, a coroutines and asynchronous loading methods are used to further improve the roaming process. The experimental results show that our method is faster than Acute3D Viewer in the visualization of oblique photography model.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"38 1","pages":"111980W - 111980W-5"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73414603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Prediction accuracy analysis with logistic regression and CART decision tree 基于logistic回归和CART决策树的预测精度分析
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540361
Xudong Zhang, Di Wang, Ying-Can Qian, Yingming Yang
{"title":"Prediction accuracy analysis with logistic regression and CART decision tree","authors":"Xudong Zhang, Di Wang, Ying-Can Qian, Yingming Yang","doi":"10.1117/12.2540361","DOIUrl":"https://doi.org/10.1117/12.2540361","url":null,"abstract":"Classification is one of the most important techniques in machine learning. In classification problems, logistic regression and decision tree are two efficient algorithms in supervised learning. In this paper, we tested logical regression and CART decision tree algorithms on different datasets. The results received from experiments showed that CART decision tree performs much better in data set with more attributes and slight imbalanced data distribution. At the same time logistic regression is more accurate on datasets with fewer attributes and balanced data distribution.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"2 1","pages":"1119810 - 1119810-7"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75539343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
A design framework for adaptive e-learning environment 自适应电子学习环境的设计框架
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540992
Xuanxi Li, Xiaoxue Wang, X. Gu
{"title":"A design framework for adaptive e-learning environment","authors":"Xuanxi Li, Xiaoxue Wang, X. Gu","doi":"10.1117/12.2540992","DOIUrl":"https://doi.org/10.1117/12.2540992","url":null,"abstract":"This study proposes an adaptive e-learning environment design framework with a focus on learner personas. Following the TPACK model, this study puts forward a framework of Persona-based Technological Pedagogical Content Design (PTPCD). The framework of PTPCD guides designers of adaptive e-learning environment through suggested and recommended Technological Pedagogical Content to the target learners with matching persona intelligently or semiintelligently. Designers are suggested to select the indicators consciously based on the pedagogy, technology, and specific content. Using data mining techniques to label personalized classification, which leads to learner personas. The PTPCD has theoretical and practical implications for designers and researchers of adaptive e-learning environment. Future studies are suggested to demonstrate and complement the framework in practice.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"392 1","pages":"111980Z - 111980Z-5"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85396760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Video-based detection and classification of driving postures by feature distance extraction and BP neutral network 基于特征距离提取和BP神经网络的视频驾驶姿态检测与分类
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540471
Hui Tang, Jie He, Youfeng Zheng, Jun Zhang, Ling Wei
{"title":"Video-based detection and classification of driving postures by feature distance extraction and BP neutral network","authors":"Hui Tang, Jie He, Youfeng Zheng, Jun Zhang, Ling Wei","doi":"10.1117/12.2540471","DOIUrl":"https://doi.org/10.1117/12.2540471","url":null,"abstract":"At present, academic research mainly focuses on detecting driver fatigue and distraction through the driver's eyes and head. But there are few studies on detecting driving behavior through the head, hands and even the body, most of which use the skin color detection method to extract a single full-image pixel as a feature and the dimension is too large, problems such as instantaneous region overlap and partial occlusion occur inevitably in the detection process, thereby affecting the detection accuracy. In this paper, we propose a driving posture detection method based on video and skin color region distance. The image features are represented by extracting the skin color region centroid coordinates of the sampled images from videos and converting them into feature distances. Then the BP neural network is used to implement the identification and classification of driving behavior, which can effectively improve the detection rate of the driving behavior, and finally realize the real-time warning of the driving process.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"20 1","pages":"111980G - 111980G-8"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86031666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Improved threshold function image denoising method 改进的阈值函数图像去噪方法
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540756
Mengtao Huang, Luomin Wang
{"title":"Improved threshold function image denoising method","authors":"Mengtao Huang, Luomin Wang","doi":"10.1117/12.2540756","DOIUrl":"https://doi.org/10.1117/12.2540756","url":null,"abstract":"In this paper, an improved threshold function is proposed for the discontinuity problem of hard threshold function and the constant deviation of original wavelet coefficients and estimated coefficients in soft threshold function. This function improves the deficiency of traditional threshold function by introducing control coefficients, and the function have certain flexibility. The experimental results show that the improved wavelet threshold function has a better effect in removing image gaussian noise than traditional threshold function. Moreover, the PSNR(peak signal to noise ratio ) and EPI (edge retention index) values of the image are both improved.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"28 1","pages":"1119808 - 1119808-5"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87847994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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