フルテキストファイル
著者
西山 正志 Graduate School of Engineering, Tottori University / Cross-informatics Research Center, Tottori University 研究者総覧 KAKEN
Matsumoto, Riku Graduate School of Engineering, Tottori University
吉村 宏紀 Graduate School of Engineering, Tottori University 研究者総覧 KAKEN
岩井 儀雄 Graduate School of Engineering, Tottori University / Cross-informatics Research Center, Tottori University 研究者総覧 KAKEN
キーワード
Gaze map
Feature extraction
Personal attributes
抄録
We discuss how to reveal and use the gaze locations of observers who view pedestrian images for personal attribute classification. Observers look at informative regions when attempting to classify the attributes of pedestrians in images. Thus, we hypothesize that the regions in which observers’ gaze locations are clustered will contain discriminative features for the classifiers of personal attributes. Our method acquires the distribution of gaze locations from several observers while they perform the task of manually classifying each personal attribute. We term this distribution a task-oriented gaze map. To extract discriminative features, we assign large weights to the region with a cluster of gaze locations in the task-oriented gaze map. In our experiments, observers mainly looked at different regions of body parts when classifying each personal attribute. Furthermore, our experiments show that the gaze-based feature extraction method significantly improved the performance of personal attribute classification when combined with a convolutional neural network or metric learning technique.
出版者
Elsevier
資料タイプ
学術雑誌論文
外部リンク
ISSN
01678655
EISSN
18727344
掲載誌名
PATTERN RECOGNITION LETTERS
112
開始ページ
241
終了ページ
248
発行日
2018-09-01
出版者DOI
著者版フラグ
著者版
著作権表記
(C) 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
掲載情報
Nishiyama Masashi, Matsumoto Riku, Yoshimura Hiroki, et al. Extracting discriminative features using task-oriented gaze maps measured from observers for personal attribute classification. PATTERN RECOGNITION LETTERS. 2018. 112. 241-248. doi:10.1016/j.patrec.2018.08.001
部局名
工学部・工学研究科
言語
英語
Web of Science Key ut
WOS:000443950800035