{"title":"SHREC ' 08条目:通过自动标签扩展训练集","authors":"Corey Goldfeder, Haoyun Feng, P. Allen","doi":"10.1109/SMI.2008.4547983","DOIUrl":null,"url":null,"abstract":"Training a 3D model classifier on a small dataset is very challenging. However, large datasets of partially classified models are now commonly available online. We use an external training set of models with associated text tags to automatically assign tags to both training and query models. The similarity between these tags, used in conjunction with a standard shape descriptor, yields a multiclassifier that outperforms the standalone shape descriptor.","PeriodicalId":118774,"journal":{"name":"2008 IEEE International Conference on Shape Modeling and Applications","volume":"268 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"SHREC’08 entry: Training set expansion via autotags\",\"authors\":\"Corey Goldfeder, Haoyun Feng, P. Allen\",\"doi\":\"10.1109/SMI.2008.4547983\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Training a 3D model classifier on a small dataset is very challenging. However, large datasets of partially classified models are now commonly available online. We use an external training set of models with associated text tags to automatically assign tags to both training and query models. The similarity between these tags, used in conjunction with a standard shape descriptor, yields a multiclassifier that outperforms the standalone shape descriptor.\",\"PeriodicalId\":118774,\"journal\":{\"name\":\"2008 IEEE International Conference on Shape Modeling and Applications\",\"volume\":\"268 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Conference on Shape Modeling and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SMI.2008.4547983\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Conference on Shape Modeling and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMI.2008.4547983","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
SHREC’08 entry: Training set expansion via autotags
Training a 3D model classifier on a small dataset is very challenging. However, large datasets of partially classified models are now commonly available online. We use an external training set of models with associated text tags to automatically assign tags to both training and query models. The similarity between these tags, used in conjunction with a standard shape descriptor, yields a multiclassifier that outperforms the standalone shape descriptor.