Ye He, Chang Xu, Nitin Khanna, Carol J Boushey, Edward J Delp
{"title":"ANALYSIS OF FOOD IMAGES: FEATURES AND CLASSIFICATION.","authors":"Ye He, Chang Xu, Nitin Khanna, Carol J Boushey, Edward J Delp","doi":"10.1109/ICIP.2014.7025555","DOIUrl":null,"url":null,"abstract":"<p><p>In this paper we investigate features and their combinations for food image analysis and a classification approach based on k-nearest neighbors and vocabulary trees. The system is evaluated on a food image dataset consisting of 1453 images of eating occasions in 42 food categories which were acquired by 45 participants in natural eating conditions. The same image dataset is used to test the classification system proposed in the previously reported work [1]. Experimental results indicate that using our combination of features and vocabulary trees for classification improves the food classification performance about 22% for the Top 1 classification accuracy and 10% for the Top 4 classification accuracy.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2014 ","pages":"2744-2748"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICIP.2014.7025555","citationCount":"75","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. International Conference on Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP.2014.7025555","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2015/1/29 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 75
Abstract
In this paper we investigate features and their combinations for food image analysis and a classification approach based on k-nearest neighbors and vocabulary trees. The system is evaluated on a food image dataset consisting of 1453 images of eating occasions in 42 food categories which were acquired by 45 participants in natural eating conditions. The same image dataset is used to test the classification system proposed in the previously reported work [1]. Experimental results indicate that using our combination of features and vocabulary trees for classification improves the food classification performance about 22% for the Top 1 classification accuracy and 10% for the Top 4 classification accuracy.
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