{"title":"一种用于分割和分组的快速领先特征向量逼近","authors":"A. Robles-Kelly, Sudeep Sarkar, E. Hancock","doi":"10.1109/ICPR.2002.1048383","DOIUrl":null,"url":null,"abstract":"We present a fast non-iterative method for approximating the leading eigenvector so as to render graph-spectral based grouping algorithms more efficient. The approximation is based on a linear perturbation analysis and applies to matrices that are non-sparse, non-negative and symmetric. For an N/spl times/N matrix, the approximation can be implemented with complexity as low as O(4N/sup 2/). We provide a performance analysis and demonstrate the usefulness of our method on image segmentation problems.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A fast leading eigenvector approximation for segmentation and grouping\",\"authors\":\"A. Robles-Kelly, Sudeep Sarkar, E. Hancock\",\"doi\":\"10.1109/ICPR.2002.1048383\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a fast non-iterative method for approximating the leading eigenvector so as to render graph-spectral based grouping algorithms more efficient. The approximation is based on a linear perturbation analysis and applies to matrices that are non-sparse, non-negative and symmetric. For an N/spl times/N matrix, the approximation can be implemented with complexity as low as O(4N/sup 2/). We provide a performance analysis and demonstrate the usefulness of our method on image segmentation problems.\",\"PeriodicalId\":159502,\"journal\":{\"name\":\"Object recognition supported by user interaction for service robots\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Object recognition supported by user interaction for service robots\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.2002.1048383\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Object recognition supported by user interaction for service robots","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.2002.1048383","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A fast leading eigenvector approximation for segmentation and grouping
We present a fast non-iterative method for approximating the leading eigenvector so as to render graph-spectral based grouping algorithms more efficient. The approximation is based on a linear perturbation analysis and applies to matrices that are non-sparse, non-negative and symmetric. For an N/spl times/N matrix, the approximation can be implemented with complexity as low as O(4N/sup 2/). We provide a performance analysis and demonstrate the usefulness of our method on image segmentation problems.
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