{"title":"基于核岭回归的张量滤波器设计","authors":"C. Bauckhage","doi":"10.1109/ICIP.2007.4379950","DOIUrl":null,"url":null,"abstract":"Tensor-based approaches to visual object detection can drastically reduce the number of parameters in the training process. Compared to their vector-based counterparts, tensor methods therefore train faster, better manage noisy or corrupted training samples, and are less prone to over-fitting. In this paper, we show how to incorporate the kernel trick into tensor-based filter design. Dealing with object detection in cluttered natural environments, the method is shown to cope with substantially varying training data and a cascade of only two kernel tensor-filters is demonstrated to provide very reliable results.","PeriodicalId":131177,"journal":{"name":"2007 IEEE International Conference on Image Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2007-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Tensor-Based Filter Design using Kernel Ridge Regression\",\"authors\":\"C. Bauckhage\",\"doi\":\"10.1109/ICIP.2007.4379950\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Tensor-based approaches to visual object detection can drastically reduce the number of parameters in the training process. Compared to their vector-based counterparts, tensor methods therefore train faster, better manage noisy or corrupted training samples, and are less prone to over-fitting. In this paper, we show how to incorporate the kernel trick into tensor-based filter design. Dealing with object detection in cluttered natural environments, the method is shown to cope with substantially varying training data and a cascade of only two kernel tensor-filters is demonstrated to provide very reliable results.\",\"PeriodicalId\":131177,\"journal\":{\"name\":\"2007 IEEE International Conference on Image Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE International Conference on Image Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIP.2007.4379950\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Conference on Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP.2007.4379950","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Tensor-Based Filter Design using Kernel Ridge Regression
Tensor-based approaches to visual object detection can drastically reduce the number of parameters in the training process. Compared to their vector-based counterparts, tensor methods therefore train faster, better manage noisy or corrupted training samples, and are less prone to over-fitting. In this paper, we show how to incorporate the kernel trick into tensor-based filter design. Dealing with object detection in cluttered natural environments, the method is shown to cope with substantially varying training data and a cascade of only two kernel tensor-filters is demonstrated to provide very reliable results.