{"title":"基于FSIFT的人脸特征点分层聚类","authors":"G. Sarwas, S. Skoneczny","doi":"10.23919/SPA.2018.8563400","DOIUrl":null,"url":null,"abstract":"In this paper a method for clustering face images based on fractional order SIFT algorithm (FSIFT) is presented. This new approach is based on the dissimilarity matrix. This matrix is constructed by using descriptors calculated for keypoints detected by FSIFT algorithm using derivatives of non integer order. To proof and compared the quality of achieved results the relative error ratio and the F-measure were applying. The final scores of experiments were compared with hierarchical clustering methods based on SIFT and SURF detectors.","PeriodicalId":265587,"journal":{"name":"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"FSIFT based feature points for face hierarchical clustering\",\"authors\":\"G. Sarwas, S. Skoneczny\",\"doi\":\"10.23919/SPA.2018.8563400\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper a method for clustering face images based on fractional order SIFT algorithm (FSIFT) is presented. This new approach is based on the dissimilarity matrix. This matrix is constructed by using descriptors calculated for keypoints detected by FSIFT algorithm using derivatives of non integer order. To proof and compared the quality of achieved results the relative error ratio and the F-measure were applying. The final scores of experiments were compared with hierarchical clustering methods based on SIFT and SURF detectors.\",\"PeriodicalId\":265587,\"journal\":{\"name\":\"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/SPA.2018.8563400\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/SPA.2018.8563400","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
FSIFT based feature points for face hierarchical clustering
In this paper a method for clustering face images based on fractional order SIFT algorithm (FSIFT) is presented. This new approach is based on the dissimilarity matrix. This matrix is constructed by using descriptors calculated for keypoints detected by FSIFT algorithm using derivatives of non integer order. To proof and compared the quality of achieved results the relative error ratio and the F-measure were applying. The final scores of experiments were compared with hierarchical clustering methods based on SIFT and SURF detectors.