{"title":"基于小波和主成分分析的尾盘壳纹理特征提取及其分类","authors":"Li-Na Song, Guangrong Ji, Jing Chen","doi":"10.1109/JCAI.2009.75","DOIUrl":null,"url":null,"abstract":"Based on wavelet and principal component analysis(PCA), an effective shell texture feature of the coscinodiscus extraction method for classification is proposed in this paper.The feature extraction process involves a normalization of the given image with different sizes followed by shift invariant wavelet transform. The shift invariant feature is computed for subband of wavelet coefficients by PCA. The rate of recognition is calculated in the end. The experiments have proved the method is effective.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Extraction of Shell Texture Feature of Coscinodiscus for Classification Based on Wavelet and PCA\",\"authors\":\"Li-Na Song, Guangrong Ji, Jing Chen\",\"doi\":\"10.1109/JCAI.2009.75\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on wavelet and principal component analysis(PCA), an effective shell texture feature of the coscinodiscus extraction method for classification is proposed in this paper.The feature extraction process involves a normalization of the given image with different sizes followed by shift invariant wavelet transform. The shift invariant feature is computed for subband of wavelet coefficients by PCA. The rate of recognition is calculated in the end. The experiments have proved the method is effective.\",\"PeriodicalId\":154425,\"journal\":{\"name\":\"2009 International Joint Conference on Artificial Intelligence\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-04-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Joint Conference on Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/JCAI.2009.75\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Joint Conference on Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/JCAI.2009.75","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Extraction of Shell Texture Feature of Coscinodiscus for Classification Based on Wavelet and PCA
Based on wavelet and principal component analysis(PCA), an effective shell texture feature of the coscinodiscus extraction method for classification is proposed in this paper.The feature extraction process involves a normalization of the given image with different sizes followed by shift invariant wavelet transform. The shift invariant feature is computed for subband of wavelet coefficients by PCA. The rate of recognition is calculated in the end. The experiments have proved the method is effective.