事前知識に基づく食事領域抽出の改良と実画像を用いた比較評価

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2021/11/01

タイトル(英語): Improvement of Food Region Extraction based on Prior Knowledge and Comparative Evaluation using Actual Images

著者名: 北田　絢子（大阪電気通信大学情報通信工学部情報工学科），二神　拓也（大阪電気通信大学情報通信工学部情報工学科），早坂　昇（大阪電気通信大学情報通信工学部情報工学科）

著者名(英語): Ayako Kitada (Department of Engineering Informatics, Osaka Electro-Communication University), Takuya Futagami (Department of Engineering Informatics, Osaka Electro-Communication University), Noboru Hayasaka (Department of Engineering Informatics, Osaka El

キーワード: 食事画像，食事領域抽出，GrabCut，極値点，凸包　　food image，food extraction，GrabCut，local extrema，convex hull

要約(英語): In this paper, we propose a method which can extract food regions from food images to improve extraction accuracy compared with a conventional method by decreasing misdetermination of the food regions as the background regions. The proposed method uses a convex hull based on the local extrema and their density to generate initial seeds for GrabCut, which can revise the food and background regions on the basis of color similarity and distribution. Our experiment demonstrated that the proposed method significantly increased the F-measure, which shows the comprehensive extraction accuracy, by 4.41% or more compared with the conventional method. The proposed method also increased the F-measure by 4.54% or more compared with SegNet based on deep convolutional neural network trained by 1017 food images available on the Internet. These results provided the fact that the proposed method was effective in the extraction accuracy compared with the existing methods which can be constructed by limited introduction cost.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.141 No.11 （2021） 特集：電気関係学会関西連合大会

本誌掲載ページ: 1197-1204 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/141/11/141_1197/_article/-char/ja/