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

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Clustering by Learning the Non-Negative Half-Space 学习非负半空间聚类
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521244
Kangheng Hu, Jinyu Tian, Yuanyan Tang
{"title":"Clustering by Learning the Non-Negative Half-Space","authors":"Kangheng Hu, Jinyu Tian, Yuanyan Tang","doi":"10.1109/ICWAPR.2018.8521244","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521244","url":null,"abstract":"This paper proposes a novel clustering algorithm which is called Non-negative Half-space Clustering (NHC), by revealing the nonnegative half-space structure of samples. The half-space is the union of some nearly independent half-spaces, and each class of samples is dominated by this half-space. Since the subspace independent assumption is not imposed on the samples, NHC is robust for the increasing of number of classes compared with other subspace clustering methods such as Sparse Space Clustering. After obtaining a half-space structure, the adjacency graph is almost block-wise, and can be well grouped by some cutting techniques. In the experiment section, we implement NHC and other competitive algorithms on two database CBCL and Reuters-21578. The result shows that NHC performs better on the two database, and more robust than SSC.","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":"126452933","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
Alleviating Over-Fitting in Attribute Reduction: An Early Stopping Strategy 缓解属性约简中的过拟合:一种早期停止策略
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521316
Keyu Liu, Jingjing Song, Wendong Zhang, Xibei Yang
{"title":"Alleviating Over-Fitting in Attribute Reduction: An Early Stopping Strategy","authors":"Keyu Liu, Jingjing Song, Wendong Zhang, Xibei Yang","doi":"10.1109/ICWAPR.2018.8521316","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521316","url":null,"abstract":"In rough set theory, forward heuristic algorithm selects the most important attribute in the process of attribute reduction until the given constraint is satisfied. However, the attributes selected by such strategy may bring us over-fitting. To solve such problem, a new heuristic algorithm is designed: the importance of the attribute is obtained by cross validation and then the Early Stopping is employed to terminate the algorithm if over-fitting occurs. Based on the neighborhood rough set, the heuristic algorithm is compared with the new method over several UCI data sets. The experimental results show that: 1) the proposed algorithm can effectively alleviate over-fitting; 2) the reduct obtained by the new algorithm may offer us better classification performances.","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":"131216968","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
Huber Collaborative Representation for Robust Face Identification 鲁棒人脸识别的Huber协同表示
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521265
Yulong Wang, Cui Zou, Yuanyan Tang, Lina Yang
{"title":"Huber Collaborative Representation for Robust Face Identification","authors":"Yulong Wang, Cui Zou, Yuanyan Tang, Lina Yang","doi":"10.1109/ICWAPR.2018.8521265","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521265","url":null,"abstract":"In this paper, we consider the problem of recognizing human faces from the facial images of front views against random corruption. To this end, we develop a Huber collaborative representation based classification (HCRC) approach and apply it to face identification. To handle gross corruption, we exploit the robust Huber estimator as the cost function. A half-quadratic optimization algorithm is devised to solve the HCRC model efficiently. The experiments on real-life data validate the efficacy of HCRC for face identification.","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":"131856497","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
Research of Localization Algorithm of Internet of Vehicles Based on Intelligent Transportation 基于智能交通的车联网定位算法研究
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521299
Musong Gu, Fang Miao, C. Gao, Zi-Shu He, W. Fan, Li Li
{"title":"Research of Localization Algorithm of Internet of Vehicles Based on Intelligent Transportation","authors":"Musong Gu, Fang Miao, C. Gao, Zi-Shu He, W. Fan, Li Li","doi":"10.1109/ICWAPR.2018.8521299","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521299","url":null,"abstract":"In the Internet of Vehicles, the real-time localization of vehicles is a very significant problem. The relative position between vehicles as well as between vehicle and Road Side Unit (RSU) is the localization data we are in more need of. When compared with the localization algorithm, the non-ranging technology is mainly adopted. In the research of the non-ranging technology, DVHop algorithm is the algorithm studied the most at present but there still exists problems such as major error of localization. Therefore, we have tried to improve it with the chemical reaction optimization and compare it with the original algorithm. Through the simulation experiment, the localization error of the improved algorithm is far lower than that of the original DVHop algorithm, largely enhancing the precision of localization. These information are valuable virtual assets which will provide more reliable basis for post-period data treatment and decision-making analysis.","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":"116323765","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
Spectral-Spatial Graph Convolutional Networks for Semel-Supervised Hyperspectral Image Classification 基于semel监督的高光谱图像分类的光谱-空间图卷积网络
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521407
Anyong Qin, Chang Liu, Zhaowei Shang, Jinyu Tian
{"title":"Spectral-Spatial Graph Convolutional Networks for Semel-Supervised Hyperspectral Image Classification","authors":"Anyong Qin, Chang Liu, Zhaowei Shang, Jinyu Tian","doi":"10.1109/ICWAPR.2018.8521407","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521407","url":null,"abstract":"Collecting label samples is quite costly and time consuming for hyperspectral image (HSI) classification tasks. Semi-supervised learning framework, which combines the intrinsic information of labeled and unlabeled samples can alleviate the deficient labeled samples and increase the accuracy of HSI classification. In this paper, we propose a novel framework for semi-supervised learning on multiple spectral-spatial graphs that is based on graph convolutional networks (SGCN). In the filtering operation on graphs we consider the spatial information and spectral signatures of HSI simultaneously. The experimental results on three real-life HSI data sets, i.e. Botswana Hyperion, Kennedy Space Center, and Indian Pines, show that the proposed SGCN can significantly improve the classification accuracy. For instance, the over accuracy on Indian Pine data is increased from 78 % to 93 %.","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":"131512331","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: 6
Object Tracking Under Occlusion Using LGEM-Trained SSVM 利用lgem训练的SSVM进行遮挡下的目标跟踪
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521283
Anqi Lin, Tingyang Wei, Wing W. Y. Ng
{"title":"Object Tracking Under Occlusion Using LGEM-Trained SSVM","authors":"Anqi Lin, Tingyang Wei, Wing W. Y. Ng","doi":"10.1109/ICWAPR.2018.8521283","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521283","url":null,"abstract":"Adaptive tracking-by-detection methods are widely used in computer vision for object tracking. Struck tracking is known as avoiding unclear intermediate labeling steps, and utilizing both the SMO and the budgeting mechanism for updating. However, the fixed budget is inflexible and heuristic, and the optimization-loop easily leads SVMs to overfitting, and the fixed combination of three kernels with specified features weakens extension capabilities. Furthermore, the quick update causes wrong learning under occlusion, considering previous “negative” samples as the current “positive” samples and drifting the tracker to that “neg-ative” samples. In this paper, we present a framework based on both the one-kernel Struck and the Localized Generalization Error Model (LGEM). By comparing the $Q$ values of Structured output SVM (SSVM) with different structures and con-troling the updating loops, a tradeoff the Optimality and Generalization is realized. Moreover, via measuring the fluctuation on $Q$ value, suitable new samples are selected for updating, tracking is simplified into using a single Gaussian kernel for further potential extension. As a result, a more generalized, occlusion-overcoming tracker is constructed. Experimentally, our algorithm is shown to be able to outperform state-of-the-art trackers on occlusion handle on various benchmark videos.","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":"134535353","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
Extracting Rules and Knowledge in Multi-Level Information Table 多层次信息表中规则和知识的提取
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521332
Bingjiao Fan, Eric C. C. Tsang, De-gang Chen, Wei-Hua Xu, Wen-tao Li
{"title":"Extracting Rules and Knowledge in Multi-Level Information Table","authors":"Bingjiao Fan, Eric C. C. Tsang, De-gang Chen, Wei-Hua Xu, Wen-tao Li","doi":"10.1109/ICWAPR.2018.8521332","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521332","url":null,"abstract":"Rules extraction is a basic issue in both knowledge representation and data mining. In this paper, we put forward an approach to rules extraction by considering the regular condition entropy and the mutual information in a multi-level information table. The multi-level information table is investigated by introducing an real life example. Then the attribute value conversion function is constructed in the multi-level information table to obtain the higher levels attribute values from the lower levels. Moreover, the thickness degree relationships between different global levels is presented in detail. Finally, an example on rule extraction from some commodities is applied and tested to illustrate the effectiveness and rationality of our method.","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":"125049621","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
A Multi-Focus Image Fusion Algorithm Based on Non-Uniform Rectangular Partition with Morphology Operation 基于形态学非均匀矩形分割的多焦点图像融合算法
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521331
Xiaoyu Liu, U. KinTak
{"title":"A Multi-Focus Image Fusion Algorithm Based on Non-Uniform Rectangular Partition with Morphology Operation","authors":"Xiaoyu Liu, U. KinTak","doi":"10.1109/ICWAPR.2018.8521331","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521331","url":null,"abstract":"Due to the shooting environment or the camera, photos may be taken at different focal points and merged into a clear photo for later use by fusion technology. This paper presents an efficient multi-focus image fusion method based on Non-uniform Rectangular Partition (NURP) [1] with morphological operations [2]. Under a certain control error, the left and right focus of the images are divided into non-uniform rectangles whose dimensions are different from different focuses for the same pixel. The NURP can roughly determine the focused and unfocused separation lines of the image according to their partition grid sizes between left and right focus images. The morphological close and open operations are performed on the fusion edge separation line to delete some noise information and keep the useful information along the edge line so that it become more close to the real separation line. Finally, the final fusion pixels are determined according to the edge boundary line to form the final fused image. Experimental results show that the proposed algorithm can enhance the spatial details of the fusion image, improve its visual quality, and obtain better objective evaluation indicators.","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":"122206835","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
Underwater Target Feature Extraction and Classification Based on Gammatone Filter and Machine Learning 基于伽玛酮滤波和机器学习的水下目标特征提取与分类
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521356
Wen Zhang, Yanqun Wu, Dezhi Wang, Yongxian Wang, Yibo Wang, Lilun Zhang
{"title":"Underwater Target Feature Extraction and Classification Based on Gammatone Filter and Machine Learning","authors":"Wen Zhang, Yanqun Wu, Dezhi Wang, Yongxian Wang, Yibo Wang, Lilun Zhang","doi":"10.1109/ICWAPR.2018.8521356","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521356","url":null,"abstract":"Underwater target radiated noise feature extraction and classification are important issues in underwater acoustic applications. In this paper., feature extraction is processed based on Gammatone filter and the target classification is processed using machine learning (ML). From the processed result of the real underwater target data, it showed that Gammatone filter is an efficient way to do feature extraction and it also has better classification accuracy compared with some other feature extracting methods. It also showed that machine learning is an efficient tool when applied in underwater target radiated noise classification where the assignment is a label to given input value.","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":"127608370","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: 9
An Estimation of Rotation and Translation in Image Separation Problem 图像分离问题中旋转和平移的估计
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521267
A. Morimoto, R. Ashino, T. Mandai
{"title":"An Estimation of Rotation and Translation in Image Separation Problem","authors":"A. Morimoto, R. Ashino, T. Mandai","doi":"10.1109/ICWAPR.2018.8521267","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521267","url":null,"abstract":"The image separation problem is considered, where observed images are weighted superpositions of translations and rotations of original images. An algorithm to estimate the number of original images, relative rotation angles, and relative translation parameters for two observed images is proposed. Numerical experiments demonstrate the usefulness of the proposed 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":"128628536","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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