{"title":"Kernel Methods in Computer Vision","authors":"Christoph H. Lampert","doi":"10.1561/0600000027","DOIUrl":null,"url":null,"abstract":"Over the last years, kernel methods have established themselves as powerful tools for computer vision researchers as well as for practitioners. In this tutorial, we give an introduction to kernel methods in computer vision from a geometric perspective, introducing not only the ubiquitous support vector machines, but also less known techniques for regression, dimensionality reduction, outlier detection, and clustering. Additionally, we give an outlook on very recent, non-classical techniques for the prediction of structure data, for the estimation of statistical dependency, and for learning the kernel function itself. All methods are illustrated with examples of successful application from the recent computer vision research literature.","PeriodicalId":45662,"journal":{"name":"Foundations and Trends in Computer Graphics and Vision","volume":"78 1","pages":"193-285"},"PeriodicalIF":3.8000,"publicationDate":"2009-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"102","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Foundations and Trends in Computer Graphics and Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1561/0600000027","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 102
Abstract
Over the last years, kernel methods have established themselves as powerful tools for computer vision researchers as well as for practitioners. In this tutorial, we give an introduction to kernel methods in computer vision from a geometric perspective, introducing not only the ubiquitous support vector machines, but also less known techniques for regression, dimensionality reduction, outlier detection, and clustering. Additionally, we give an outlook on very recent, non-classical techniques for the prediction of structure data, for the estimation of statistical dependency, and for learning the kernel function itself. All methods are illustrated with examples of successful application from the recent computer vision research literature.
期刊介绍:
The growth in all aspects of research in the last decade has led to a multitude of new publications and an exponential increase in published research. Finding a way through the excellent existing literature and keeping up to date has become a major time-consuming problem. Electronic publishing has given researchers instant access to more articles than ever before. But which articles are the essential ones that should be read to understand and keep abreast with developments of any topic? To address this problem Foundations and Trends® in Computer Graphics and Vision publishes high-quality survey and tutorial monographs of the field.
Each issue of Foundations and Trends® in Computer Graphics and Vision comprises a 50-100 page monograph written by research leaders in the field. Monographs that give tutorial coverage of subjects, research retrospectives as well as survey papers that offer state-of-the-art reviews fall within the scope of the journal.