R. Fukui, Keisuke Maeda, Masahiko Watanabe, M. Shimosaka, Tomomasa Sato
{"title":"Numerical modeling of image discriminability for home storage and organization system on a smart device","authors":"R. Fukui, Keisuke Maeda, Masahiko Watanabe, M. Shimosaka, Tomomasa Sato","doi":"10.1145/2494091.2494153","DOIUrl":null,"url":null,"abstract":"In Home storage and organization system on a smart device, thumbnail pictures (Tag Image) of daily-use objects are often used. Discriminability of Tag Image is important to realize superior usability. In this paper, we have tried to construct a numerical model of Tag Image's discriminabillity. The proposed model is based on simple linear regression from popular image features and their statistics. In addition, web-based data input system has also been developed to collect training data efficiently. Consequently, the input system has acquired a substantial number of data and a numerical model has been constructed. The constructed model has substantially good but not perfect performance.","PeriodicalId":220524,"journal":{"name":"Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication","volume":"112 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2494091.2494153","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In Home storage and organization system on a smart device, thumbnail pictures (Tag Image) of daily-use objects are often used. Discriminability of Tag Image is important to realize superior usability. In this paper, we have tried to construct a numerical model of Tag Image's discriminabillity. The proposed model is based on simple linear regression from popular image features and their statistics. In addition, web-based data input system has also been developed to collect training data efficiently. Consequently, the input system has acquired a substantial number of data and a numerical model has been constructed. The constructed model has substantially good but not perfect performance.