Shaobo Fang, Fengqing Zhu, Carol J Boushey, Edward J Delp
{"title":"THE USE OF CO-OCCURRENCE PATTERNS IN SINGLE IMAGE BASED FOOD PORTION ESTIMATION.","authors":"Shaobo Fang, Fengqing Zhu, Carol J Boushey, Edward J Delp","doi":"10.1109/GlobalSIP.2017.8308685","DOIUrl":null,"url":null,"abstract":"<p><p>Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. Food portions estimation is a challenging problem as food preparation and consumption process pose large variations on food shapes and appearances. We use geometric model based technique to estimate food portions and further improve estimation accuracy using co-occurrence patterns. We estimate the food portion co-occurrence patterns from food images we collected from dietary studies using the mobile Food Record (mFR) system we developed. Co-occurrence patterns is used as prior knowledge to refine portion estimation results. We show that the portion estimation accuracy has been improved when incorporating the co-occurrence patterns as contextual information.</p>","PeriodicalId":91429,"journal":{"name":"... IEEE Global Conference on Signal and Information Processing. IEEE Global Conference on Signal and Information Processing","volume":"2017 ","pages":"462-466"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6226047/pdf/nihms-995024.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"... IEEE Global Conference on Signal and Information Processing. IEEE Global Conference on Signal and Information Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GlobalSIP.2017.8308685","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2018/3/8 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. Food portions estimation is a challenging problem as food preparation and consumption process pose large variations on food shapes and appearances. We use geometric model based technique to estimate food portions and further improve estimation accuracy using co-occurrence patterns. We estimate the food portion co-occurrence patterns from food images we collected from dietary studies using the mobile Food Record (mFR) system we developed. Co-occurrence patterns is used as prior knowledge to refine portion estimation results. We show that the portion estimation accuracy has been improved when incorporating the co-occurrence patterns as contextual information.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
