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Extracting discriminative features using task-oriented gaze maps measured from observers for personal attribute classification
https://repository.lib.tottori-u.ac.jp/records/7321
https://repository.lib.tottori-u.ac.jp/records/7321cd26a227-5fe0-4280-8de5-7ddc46ecd983
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
prl112_241.pdf (1.6 MB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||
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| 公開日 | 2022-08-03 | |||||
| タイトル | ||||||
| タイトル | Extracting discriminative features using task-oriented gaze maps measured from observers for personal attribute classification | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Gaze map | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Feature extraction | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Personal attributes | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Gaze map | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Feature extraction | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Personal attributes | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| 著者 |
西山, 正志
× 西山, 正志× 吉村, 宏紀× 岩井, 儀雄× Matsumoto, Riku |
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| 著者所属（英） | ||||||
| 言語 | en | |||||
| 値 | Graduate School of Engineering, Tottori University / Cross-informatics Research Center, Tottori University | |||||
| 著者所属（英） | ||||||
| 言語 | en | |||||
| 値 | Graduate School of Engineering, Tottori University | |||||
| 著者所属（英） | ||||||
| 言語 | en | |||||
| 値 | Graduate School of Engineering, Tottori University | |||||
| 著者所属（英） | ||||||
| 言語 | en | |||||
| 値 | Graduate School of Engineering, Tottori University / Cross-informatics Research Center, Tottori University | |||||
| 抄録 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | 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. | |||||
| 書誌情報 |
PATTERN RECOGNITION LETTERS en : PATTERN RECOGNITION LETTERS 巻 112, p. 241-248, 発行日 2018-09-01 |
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| 出版者 | ||||||
| 出版者 | Elsevier | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 01678655 | |||||
| DOI | ||||||
| 関連タイプ | isVersionOf | |||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1016/j.patrec.2018.08.001 | |||||
| 権利 | ||||||
| 権利情報 | (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/ | |||||
| 情報源 | ||||||
| 関連名称 | 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.patr | |||||
| 関連サイト | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://www.sciencedirect.com/science/article/pii/S0167865518303696 | |||||
| 関連名称 | https://www.sciencedirect.com/science/article/pii/S0167865518303696 | |||||
| 著者版フラグ | ||||||
| 出版タイプ | AM | |||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||
| EISSN | ||||||
| 値 | 18727344 | |||||
