{"title":"图像分类中特定特征分类器的自适应委员会","authors":"F. Falchi, T. Fagni, F. Sebastiani","doi":"10.5220/0001968501130122","DOIUrl":null,"url":null,"abstract":"We present a system for image classification based on an adaptive committee of five classifiers, each specialized on classifying images based on a single MPEG-7 feature. We test four different ways to set up such a committee, and obtain important accuracy improvements with respect to a baseline in which a single classifier, working an all five features at the same time, is employed.","PeriodicalId":44543,"journal":{"name":"ERCIM News","volume":null,"pages":null},"PeriodicalIF":0.1000,"publicationDate":"2016-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Adaptive Committees of Feature-Specific Classifiers for Image Classification\",\"authors\":\"F. Falchi, T. Fagni, F. Sebastiani\",\"doi\":\"10.5220/0001968501130122\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a system for image classification based on an adaptive committee of five classifiers, each specialized on classifying images based on a single MPEG-7 feature. We test four different ways to set up such a committee, and obtain important accuracy improvements with respect to a baseline in which a single classifier, working an all five features at the same time, is employed.\",\"PeriodicalId\":44543,\"journal\":{\"name\":\"ERCIM News\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.1000,\"publicationDate\":\"2016-12-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ERCIM News\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5220/0001968501130122\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERCIM News","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0001968501130122","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Adaptive Committees of Feature-Specific Classifiers for Image Classification
We present a system for image classification based on an adaptive committee of five classifiers, each specialized on classifying images based on a single MPEG-7 feature. We test four different ways to set up such a committee, and obtain important accuracy improvements with respect to a baseline in which a single classifier, working an all five features at the same time, is employed.
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