『徳島大学 教育・研究者情報データベース (EDB)』---[学外] /
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EID=335353EID:335353, Map:0, LastModified:2018年3月12日(月) 13:41:50, Operator:[三好 小文], Avail:TRUE, Censor:0, Owner:[松本 和幸], Read:継承, Write:継承, Delete:継承.
種別 (必須): 国際会議 [継承]
言語 (必須): 英語 [継承]
招待 (推奨):
審査 (推奨): Peer Review [継承]
カテゴリ (推奨): 研究 [継承]
共著種別 (推奨): 単独著作 (徳島大学内の単一の研究グループ(研究室等)内の研究 (単著も含む)) [継承]
学究種別 (推奨): 博士課程学生による研究報告 [継承]
組織 (推奨):
著者 (必須): 1. (英) Fujisawa Akira (日) 藤澤 日明 (読) ふじさわ あきら
役割 (任意): 共著 [継承]
貢献度 (任意):
学籍番号 (推奨): **** [ユーザ]
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2.松本 和幸 ([徳島大学.大学院社会産業理工学研究部.理工学域.知能情報系.情報工学分野]/[徳島大学.理工学部.理工学科.情報光システムコース.情報工学講座])
役割 (任意): 共著 [継承]
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[継承]
3.吉田 稔 ([徳島大学.大学院社会産業理工学研究部.理工学域.知能情報系.情報工学分野]/[徳島大学.理工学部.理工学科.情報光システムコース.情報工学講座])
役割 (任意): 共著 [継承]
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[継承]
4.北 研二 ([徳島大学.大学院社会産業理工学研究部.理工学域.知能情報系.情報工学分野]/[徳島大学.理工学部.理工学科.情報光システムコース.情報工学講座])
役割 (任意): 共著 [継承]
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[継承]
題名 (必須): (英) Facial Expression Classification Based on Shape Feature of Emoticons  (日) 顔文字の形状特徴に基づく表情分類   [継承]
副題 (任意):
要約 (任意): (英) Emoticons are used in the situation of textual communication such as web mails and internet forums. Many of the existing studies dealing with classification or extraction of emoticons regard emoticons as a kind of character string and focus on what characters constitute the emoticons or how they are lined up. However, emoticons are used to express human facial expressions, and characters constituting them represent various facial parts such as eyes, nose, mouth, etc. Such characters can be identified as different facial parts depending on their positions, and facial expressions are thought to be represented by the combinations of their shape features. In this study, we classified the facial expressions of emoticons by focusing on the shape features of those emoticons. To deal with shape features of emoticons, we converted emoticons, which are text data, to image data. Emoticons are mainly formed by line segments of characters, and use only black and white colors. Therefore, other factors such as colors and shades were not considered as the feature to classify the facial expressions. In the experiments, we used image features that did not require color information. As the result of comparative experiment with the 1-nearest neighbor method using character features, the facial expression recognition rate is 52% when using the Histograms of Oriented Gradients(HOG) was used as image feature. By this result, proposed method improved recognition rate by 2 % than using baseline.  (日)    [継承]
キーワード (推奨): 1. (英) facial expression classification (日) (読) [継承]
2. (英) emoticon (日) 顔文字 (読) [継承]
3. (英) image feature (日) 画像特徴 (読) [継承]
発行所 (推奨):
誌名 (必須): (英) Proceedings of 1st International Conference on Machine Learning and Data Engineering (iCMLDE2017) (日) (読)
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(必須): 29 34 [継承]
都市 (必須): シドニー (Sydney/[オーストラリア]) [継承]
年月日 (必須): 西暦 2017年 11月 20日 (平成 29年 11月 20日) [継承]
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標準的な表示

和文冊子 ● Akira Fujisawa, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Facial Expression Classification Based on Shape Feature of Emoticons, Proceedings of 1st International Conference on Machine Learning and Data Engineering (iCMLDE2017), 29-34, Sydney, Nov. 2017.
欧文冊子 ● Akira Fujisawa, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Facial Expression Classification Based on Shape Feature of Emoticons, Proceedings of 1st International Conference on Machine Learning and Data Engineering (iCMLDE2017), 29-34, Sydney, Nov. 2017.

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Number of session users = 6, LA = 1.43, Max(EID) = 376489, Max(EOID) = 1008302.