2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)最新文献

Feature Extraction Based on the Wavelets and Persistent Homology for Early Esophageal Cancer Detection From Endoscopic Image 基于小波和持续同源性的早期食管癌内镜图像特征提取

2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521329

H. Omura, Teruya Minamoto

{"title":"Feature Extraction Based on the Wavelets and Persistent Homology for Early Esophageal Cancer Detection From Endoscopic Image","authors":"H. Omura, Teruya Minamoto","doi":"10.1109/ICWAPR.2018.8521329","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521329","url":null,"abstract":"A new feature extraction method based on the wavelets and persistent homology for early esophageal cancer detection from an endoscopic image is proposed. In our proposed method, an input endoscopic image is converted to CIE L*a*b* color spaces, and a fusion image is made from the a* and b* components. Applying the two types of wavelets to the fusion image, the two types of frequency components are obtained. One is the low frequency component obtained by the dyadic wavelet transform (DYWT), and the other is the high frequency components obtained by the dual-tree complex discrete wavelet transform (DT-CDWT). Applying the dynamic threshold to each frequency component, binary images are obtained, and then each binary image is divided into small blocks. Utilizing the persistent homology to each block, the new features of the input image are acquired. The method to extract the feature is described in detail, and experimental results are presented to demonstrate that our method is useful for the development of early esophageal cancer detection from endoscopic image.","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125893173","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

Graph-Based Sparse Matrix Regression for 2D Feature Selection 基于图的稀疏矩阵回归二维特征选择

2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521279

Junyuli, Haoliang Yuan, L. L. Lai, Houqing Zheng, W. Qian, Xiaoming Zhou

引用次数: 0

SGL-RFS: Semi-Supervised Graph Learning Robust Feature Selection 半监督图学习鲁棒特征选择

2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521274

Junjie Zheng, Haoliang Yuan, L. L. Lai, Houqing Zheng, Zhimin Wang, Fenghua Wang

引用次数: 5

Two-Layer Localized Sensitive Hashing with Adaptive Re-Ranking 具有自适应重排序的两层局部敏感哈希

2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521325

Wing W. Y. Ng, Si-chao Lei, Xing Tian

{"title":"Two-Layer Localized Sensitive Hashing with Adaptive Re-Ranking","authors":"Wing W. Y. Ng, Si-chao Lei, Xing Tian","doi":"10.1109/ICWAPR.2018.8521325","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521325","url":null,"abstract":"Hashing-based approximated nearest neighbor (ANN) search techniques have been widely studied owing to its compact binary codes and efficient search scheme for large-scale image retrieval. For the most popular existing hashing methods, e.g. the Locality sensitivity Hashing and the Spectral Hashing, the key issue is to choose appropriate binary code length for similarity preserving and computational efficiency. Several extensions have been proposed to address the problem of balancing precision, recall rate and computation efficiency. However, most of existing hashing methods struggle for how to choose appropriate length of hash codes. A bad choice of code length may result in extremely poor performance of retrieval. In this paper, we propose a novel hashing scheme, called the Two-Layer Localized Sensitive Hashing with Adaptive Reranking (TL-LSHAR). This method utilizes a short hash code to generate the weights for a long hash code to further improve the retrieval performance. Moreover, the new scheme can be used by most of the existing hashing methods. The performance is evaluated on two large scale image databases which demonstrates the efficiency of our scheme.","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125987266","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

Some Useful Results Associated with Right-Sided Quaternion Fourier Transform 关于右侧四元数傅里叶变换的一些有用结果

2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521394

M. Bahri, R. Ashino

引用次数: 1

An Empirical Study of Shape Recognition in Ensemble Learning Context 集成学习环境下形状识别的实证研究

2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521305

Weili Ding, Xinming Wang, Han Liu, Bo Hu

{"title":"An Empirical Study of Shape Recognition in Ensemble Learning Context","authors":"Weili Ding, Xinming Wang, Han Liu, Bo Hu","doi":"10.1109/ICWAPR.2018.8521305","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521305","url":null,"abstract":"Shape recognition has been a popular application of machine learning, where each shape is defined as a class for training classifiers that recognize the shapes of new instances. Since training of classifiers is essentially achieved through learning from features, it is crucial to extract and select a set of relevant features that can effectively distinguish one class from other classes. However, different instances could present features which are highly dissimilar, even if these instances belong to the same class. The above difference in feature representation can also result in high diversity among classifiers trained by using different algorithms or data samples. In this paper, we investigate the impact of multi-classifier fusion on shape recognition by using six features extracted from a 2D shape data set. In particular, popular single learning algorithms, such as Decision Trees, Support Vector Machine and K Nearest Neighbours, are adopted to train base classifiers on features selected by using a wrapper approach. Furthermore, two popular ensemble learning algorithms (Random Forests and Gradient Boosted Trees) are adopted to train decision tree ensembles on the same feature sets. The outputs of the two ensemble classifiers are finally combined with the outputs of all the other base classifiers The experimental results show the effectiveness of the above setting of multi-classifier fusion for advancing the performance in comparison with using each single (non-ensemble) learning algorithm.","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131747489","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: 5

Irrational-Dilation Orthonormal Wavelet Basis and its Calculation Method 非理性膨胀正交小波基及其计算方法

2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521257

H. Toda, Zhong Zhang

引用次数: 2

Computer-Assisted Non-Invasive Diabetes Mellitus Detection System via Facial Key Block Analysis 基于面部键块分析的计算机辅助无创糖尿病检测系统

2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521271

Ting Shu, Bob Zhang, Yuanyan Tang

{"title":"Computer-Assisted Non-Invasive Diabetes Mellitus Detection System via Facial Key Block Analysis","authors":"Ting Shu, Bob Zhang, Yuanyan Tang","doi":"10.1109/ICWAPR.2018.8521271","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521271","url":null,"abstract":"A Computer-assisted Non-invasive Diabetes Mellitus Detection System through facial key block analysis is designed and developed in this paper. There are four main steps in our system: facial image capture through a non-invasive device, automatic location of the key blocks based on the positions of the two pupils, key block texture feature extraction using Local Binary Pattern with cell-size 21, and classification with Support Vector Machines. In the first step of this system, a specially designed facial image capture device has been developed to capture the facial image of each patient in a standard designed environment. According to Traditional Chinese Medicine theory, various facial regions can reflect the health status of different inner organs. Based on this, four key blocks are located automatically using the positions of the two pupils and used in Diabetes Mellitus detection instead of employing the whole facial image. For the last two steps, an experiment which selects the best value of Local Binary Pattern cell-size and the better classifier of two traditional classifiers (k-Nearest Neighbors and Support Vector Machines) is implemented and its results are applied in this system. In order to test the system performance, the facial images of 200 volunteers consisting of 100 Diabetes Mellitus patients and 100 healthy persons are captured and analyzed through this system. Based on the test result, the Computer-assisted Non-invasive Diabetes Mellitus Detection System through facial key block analysis is proven to be effective and efficient at distinguishing Diabetes Mellitus from Healthy patients in real time.","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123632392","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

Self-Correlation Analysis Framework with Property Data in Master Data Management — A Case on Power Utility Equipment Retire Analysis 主数据管理中属性数据的自相关分析框架——以电力设备退役分析为例

2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521284

Zhongwen Qian, Fenghua Wang, Wanli Wu, Jingzhou Cheng, Yue Wang, Fangyuan Xu, L. Lai

引用次数: 1

Proceedings of 2018 International Conference on Wavelet Analysis and Pattern Recognition 2018年小波分析与模式识别国际会议论文集

2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/icwapr.2018.8521322

引用次数: 0