Pub Date : 2019-07-01DOI: 10.1109/ICWAPR48189.2019.8946462
Jinyu Tian, Na Hu, Timothy C. H. Kwong, Yuanyan Tang
In this work, we propose a novel clustering framework by gradually shrinking the graph of samples called adaptive graph shrinking (AGS). It is motivated by the smoothness of graph signal which will reach a lower bound when samples from the same cluster merge into one component of a graph. We mimic the merging process by using some dynamic clients to represent original samples. The dynamic nature of representatives also reduces to a dynamic graph which endows the final stable graph a lower smoothness, whereas the previous work robust continuous clustering (RCC) uses a fixed graph. This dynamic process is realized by alternatively optimizing the representatives and weights of the graph. We perform experiments on two public database COIL20 and MNIST to demonstrate that the dynamically shrinking of the graph is able to promote the clustering performance.
{"title":"Clustering By Adaptive Graph Shrinking","authors":"Jinyu Tian, Na Hu, Timothy C. H. Kwong, Yuanyan Tang","doi":"10.1109/ICWAPR48189.2019.8946462","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946462","url":null,"abstract":"In this work, we propose a novel clustering framework by gradually shrinking the graph of samples called adaptive graph shrinking (AGS). It is motivated by the smoothness of graph signal which will reach a lower bound when samples from the same cluster merge into one component of a graph. We mimic the merging process by using some dynamic clients to represent original samples. The dynamic nature of representatives also reduces to a dynamic graph which endows the final stable graph a lower smoothness, whereas the previous work robust continuous clustering (RCC) uses a fixed graph. This dynamic process is realized by alternatively optimizing the representatives and weights of the graph. We perform experiments on two public database COIL20 and MNIST to demonstrate that the dynamically shrinking of the graph is able to promote the clustering performance.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124179352","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}
Pub Date : 2019-07-01DOI: 10.1109/icwapr48189.2019.8946450
{"title":"ICWAPR 2019 Organizing Committee","authors":"","doi":"10.1109/icwapr48189.2019.8946450","DOIUrl":"https://doi.org/10.1109/icwapr48189.2019.8946450","url":null,"abstract":"","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124129287","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}
Pub Date : 2019-07-01DOI: 10.1109/icwapr48189.2019.8946457
{"title":"ICWAPR 2019 Greetings from the Program Chairs","authors":"","doi":"10.1109/icwapr48189.2019.8946457","DOIUrl":"https://doi.org/10.1109/icwapr48189.2019.8946457","url":null,"abstract":"","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133799401","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}
Pub Date : 2019-07-01DOI: 10.1109/ICWAPR48189.2019.8946455
M. Bahri, R. Ashino
Firstly, based on basic properties of the kernel function of the quaternion Fourier transform we derive in detail relationships among three definitions of the quaternion Fourier transforms. Secondly, based on the quaternion Fourier transform of the quaternion Gaussian function we derive an inversion formula to recovering a quaternion function from the quaternion Fourier transform.
{"title":"Relationship Among Three Definitions Of Quaternion Fourier Transforms And Inversion Formula","authors":"M. Bahri, R. Ashino","doi":"10.1109/ICWAPR48189.2019.8946455","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946455","url":null,"abstract":"Firstly, based on basic properties of the kernel function of the quaternion Fourier transform we derive in detail relationships among three definitions of the quaternion Fourier transforms. Secondly, based on the quaternion Fourier transform of the quaternion Gaussian function we derive an inversion formula to recovering a quaternion function from the quaternion Fourier transform.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125550489","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}
Pub Date : 2019-07-01DOI: 10.1109/ICWAPR48189.2019.8946488
K. Fujinoki
We study simple linear orthogonal transforms for images, which are real-valued, non-overlapped, and fast transforms similar to the Haar wavelet transform. These transforms reveal the correlation of each block of four neighbor points (or pixels) of an image, and can thus be used for an efficient representation of the image. Since several parameters are necessary to determine the orthogonality of such transforms, we conducted numerical experiments to determine the optimal parameter for minimizing distortion errors in nonlinear image approximations.
{"title":"Nonlinear Approximation of Images with Haar-Like Four-Point Orthogonal Transforms","authors":"K. Fujinoki","doi":"10.1109/ICWAPR48189.2019.8946488","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946488","url":null,"abstract":"We study simple linear orthogonal transforms for images, which are real-valued, non-overlapped, and fast transforms similar to the Haar wavelet transform. These transforms reveal the correlation of each block of four neighbor points (or pixels) of an image, and can thus be used for an efficient representation of the image. Since several parameters are necessary to determine the orthogonality of such transforms, we conducted numerical experiments to determine the optimal parameter for minimizing distortion errors in nonlinear image approximations.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131314768","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}
Since the target data are high-dimensional, limited and class-unbalanced distribution in most real-world classification, most conventional classification methods can hardly achieve good classification results on these data. To explore an effective solution, this paper proposes the Siamese Parallel Fully-connected Neural Network (SPFCNN) as a binary classifier and uses the SMOTE method to deal with the problem of class-unbalanced data distribution. Given that classified cases naturally come with costs, cost-sensitive learning is used to improve the performance of the proposed SPFCNN. An extensive computational study is also performed on cost-sensitive SPFCNN, and the results show that the performance of the proposed approach is better than that of the comparison methods.
{"title":"Cost-Sensitive SPFCNN Miner for Classification of Imbalanced Data","authors":"Linchang Zhao, Zhaowei Shang, Ling Zhao, Yu Wei, Yuanyan Tang","doi":"10.1109/ICWAPR48189.2019.8946485","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946485","url":null,"abstract":"Since the target data are high-dimensional, limited and class-unbalanced distribution in most real-world classification, most conventional classification methods can hardly achieve good classification results on these data. To explore an effective solution, this paper proposes the Siamese Parallel Fully-connected Neural Network (SPFCNN) as a binary classifier and uses the SMOTE method to deal with the problem of class-unbalanced data distribution. Given that classified cases naturally come with costs, cost-sensitive learning is used to improve the performance of the proposed SPFCNN. An extensive computational study is also performed on cost-sensitive SPFCNN, and the results show that the performance of the proposed approach is better than that of the comparison methods.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133216837","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}
Pub Date : 2019-07-01DOI: 10.1109/icwapr48189.2019.8946480
{"title":"ICWAPR 2019 Program Committee","authors":"","doi":"10.1109/icwapr48189.2019.8946480","DOIUrl":"https://doi.org/10.1109/icwapr48189.2019.8946480","url":null,"abstract":"","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122509202","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}
As the consumption of the fossil fuel and the other unrenewable energy, the renewable energy gradually cause the high attention and become a tendency of the power production. In the solar industry, the prediction of the solar irradiation intensity is an important procedure during the photovoltaic power generation. But sometimes the solar data, which could be used to predict the future power generation case and hold the economic operation, is difficult to capture because the actual geographical environment, where is not allowed to put up the sensor. In order to solve this dilemma, this paper proposes a method of the data generation, which mainly consist of three factors. Through the comparison of the actual solar irradiation intensity data and the generated data with the method, the error is in the permitted range, which means the validity of the method.
{"title":"Data Generation Method Based on Correlation Between Sensors in Photovoltaic Arrays","authors":"Zekai Lee, Linyu Wang, Yongshen Wen, Ruixin Tang, Yiliang Fan, Xin Liang, Yu Nan, Ruiqing Song","doi":"10.1109/ICWAPR48189.2019.8946479","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946479","url":null,"abstract":"As the consumption of the fossil fuel and the other unrenewable energy, the renewable energy gradually cause the high attention and become a tendency of the power production. In the solar industry, the prediction of the solar irradiation intensity is an important procedure during the photovoltaic power generation. But sometimes the solar data, which could be used to predict the future power generation case and hold the economic operation, is difficult to capture because the actual geographical environment, where is not allowed to put up the sensor. In order to solve this dilemma, this paper proposes a method of the data generation, which mainly consist of three factors. Through the comparison of the actual solar irradiation intensity data and the generated data with the method, the error is in the permitted range, which means the validity of the method.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115100601","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}
Pub Date : 2019-07-01DOI: 10.1109/ICWAPR48189.2019.8946484
Jin Tan, Taiping Zhang, Yuanyan Tang
In recent years, sparse representation of vector signals has been successfully applied in the field of pattern recognition. However, this approach can not be used for single image, as it may require the dictionary to be overcomplete. In addition, the sparse coefficients lack some geometric explanations. This work proposes a novel sparse coding technique on single image. This sparse coding coefficients have explicitly the geometric explanations of images. It depicts the structure information of the image which is robust to variations in illumination, expression, and occlusion. Therefore, the sparse coding coefficients can be used for feature representation of images on small sample case. Experiments on face databases demonstrate the effectiveness of the new sparse coding model.
{"title":"Sparse Representation On Single Image","authors":"Jin Tan, Taiping Zhang, Yuanyan Tang","doi":"10.1109/ICWAPR48189.2019.8946484","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946484","url":null,"abstract":"In recent years, sparse representation of vector signals has been successfully applied in the field of pattern recognition. However, this approach can not be used for single image, as it may require the dictionary to be overcomplete. In addition, the sparse coefficients lack some geometric explanations. This work proposes a novel sparse coding technique on single image. This sparse coding coefficients have explicitly the geometric explanations of images. It depicts the structure information of the image which is robust to variations in illumination, expression, and occlusion. Therefore, the sparse coding coefficients can be used for feature representation of images on small sample case. Experiments on face databases demonstrate the effectiveness of the new sparse coding model.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127697741","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}
Pub Date : 2019-07-01DOI: 10.1109/icwapr48189.2019.8946475
{"title":"ICWAPR 2019 Author Index","authors":"","doi":"10.1109/icwapr48189.2019.8946475","DOIUrl":"https://doi.org/10.1109/icwapr48189.2019.8946475","url":null,"abstract":"","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129467584","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}
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