{"title":"一种提高SIFT算法匹配效率的方法","authors":"Daixian Zhu, Xiaohua Wang","doi":"10.1109/CISP.2009.5304375","DOIUrl":null,"url":null,"abstract":"Due to the good invariance of scale, rotation, illumination, SIFT (Scale Invariant Feature Transform) descriptor is commonly used in image matching. but its algorithm is complicated and computation time is long. To improve SIFT feature matching algorithm efficiency, the method of reducing similar measure matching cost is mentioned. Euclidean distance is replaced by the linearcombination of cityblock distance and chessboard distance, and reduce character point in calculating with results of part feature. The experimental results show that the algorithm can reduce the rate of time complexity and maintain robust quality at the same time, image matching efficiency is improved. Keywords-feature matching ; feature extraction; SIFT;","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A Method of Improving SIFT Algorithm Matching Efficiency\",\"authors\":\"Daixian Zhu, Xiaohua Wang\",\"doi\":\"10.1109/CISP.2009.5304375\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Due to the good invariance of scale, rotation, illumination, SIFT (Scale Invariant Feature Transform) descriptor is commonly used in image matching. but its algorithm is complicated and computation time is long. To improve SIFT feature matching algorithm efficiency, the method of reducing similar measure matching cost is mentioned. Euclidean distance is replaced by the linearcombination of cityblock distance and chessboard distance, and reduce character point in calculating with results of part feature. The experimental results show that the algorithm can reduce the rate of time complexity and maintain robust quality at the same time, image matching efficiency is improved. Keywords-feature matching ; feature extraction; SIFT;\",\"PeriodicalId\":263281,\"journal\":{\"name\":\"2009 2nd International Congress on Image and Signal Processing\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 2nd International Congress on Image and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CISP.2009.5304375\",\"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 2nd International Congress on Image and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISP.2009.5304375","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Method of Improving SIFT Algorithm Matching Efficiency
Due to the good invariance of scale, rotation, illumination, SIFT (Scale Invariant Feature Transform) descriptor is commonly used in image matching. but its algorithm is complicated and computation time is long. To improve SIFT feature matching algorithm efficiency, the method of reducing similar measure matching cost is mentioned. Euclidean distance is replaced by the linearcombination of cityblock distance and chessboard distance, and reduce character point in calculating with results of part feature. The experimental results show that the algorithm can reduce the rate of time complexity and maintain robust quality at the same time, image matching efficiency is improved. Keywords-feature matching ; feature extraction; SIFT;