Naoki Saito, Takahiro Ogawa, Satoshi Asamizu, M. Haseyama
{"title":"A tourism category classification method based on estimation of reliable decision","authors":"Naoki Saito, Takahiro Ogawa, Satoshi Asamizu, M. Haseyama","doi":"10.1109/GCCE.2016.7800331","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a tourism category classification method based on estimation of reliable decision. The proposed method performs tourism category classification using location, visual, and textual tag features obtained from tourism images in image sharing services. As the biggest contribution of this paper, the proposed method performs successful classification based on two classification results obtained from a fuzzy K-nearest neighbor algorithm using the location features and a decision level fusion approach using the visual and textual tag features. The proposed method enables estimation of reliable decision from above two classifiers.","PeriodicalId":416104,"journal":{"name":"2016 IEEE 5th Global Conference on Consumer Electronics","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 5th Global Conference on Consumer Electronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GCCE.2016.7800331","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, we propose a tourism category classification method based on estimation of reliable decision. The proposed method performs tourism category classification using location, visual, and textual tag features obtained from tourism images in image sharing services. As the biggest contribution of this paper, the proposed method performs successful classification based on two classification results obtained from a fuzzy K-nearest neighbor algorithm using the location features and a decision level fusion approach using the visual and textual tag features. The proposed method enables estimation of reliable decision from above two classifiers.