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Impression estimation model for textures using Big Data

Research Project

Project/Area Number 18K11512
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Review Section Basic Section 61060:Kansei informatics-related
Research InstitutionUniversity of Nagasaki (2020-2021)
Kwansei Gakuin University (2018-2019)

Principal Investigator

Tobitani Kensuke  長崎県立大学, 情報システム学部, 准教授 (50597333)

Co-Investigator(Kenkyū-buntansha) 片平 建史  関西学院大学, 理工学部, 講師 (40642129)
橋本 翔  関西学院大学, 理工学部, 助教 (80756700)
Project Period (FY) 2018-04-01 – 2022-03-31
Project Status Completed (Fiscal Year 2021)
Budget Amount *help
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2020: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2019: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Keywords感性的質感 / 機械学習 / 画像生成 / テクスチャ / CNN / ビッグデータ
Outline of Final Research Achievements

In this study, we proposed an image generation method for textures with desired visual sensory texture. First, (1) subjective evaluation experiments were conducted on texture images to quantify the sensory texture. Next, (2) style features were extracted using a pre-trained VGG19. Then, (3) a sensitivity evaluation model was constructed by formulating the relationship between the quantified sensory quality and the extracted style features. Finally, (4) based on the obtained model, style features were calculated when the desired emotional quality was exaggerated, and images were generated by optimization. Furthermore, the effectiveness of the method was demonstrated through validation experiments, which confirmed that the emotional quality of the generated images was significantly improved compared to the original images.

Academic Significance and Societal Importance of the Research Achievements

近年では E コマースの普及による市場環境のグローバル化に伴い,ユーザニーズや好みの多様化が進み,プロダクトのカスタマイズ化やパーソナライズ化に対する要求が高まっている.その実現に向け,人の嗜好や満足といった感性価値を的確に把握し,それらを具体的なデザインに展開する方法が注目されている.本研究により得られる成果は,直観的な素材の質感表現を可能にするという点で,人の嗜好や満足といった感性価値に基づくデザイン支援の一助になり得る.

Report

(5 results)
  • 2021 Annual Research Report   Final Research Report ( PDF )
  • 2020 Research-status Report
  • 2019 Research-status Report
  • 2018 Research-status Report
  • Research Products

    (15 results)

All 2021 2020 2019 2018 Other

All Journal Article (4 results) (of which Peer Reviewed: 1 results) Presentation (10 results) (of which Int'l Joint Research: 3 results) Remarks (1 results)

  • [Journal Article] Modeling of “High-Class Feeling” on a Cosmetic Package Design2021

    • Author(s)
      TOBITANI Kensuke、SHIRAIWA Aya、KATAHIRA Kenji、NAGATA Noriko、NIKATA Kunio、ARAKAWA Kaoru
    • Journal Title

      Journal of the Japan Society for Precision Engineering

      Volume: 87 Issue: 1 Pages: 134-139

    • DOI

      10.2493/jjspe.87.134

    • NAID

      130007965931

    • Related Report
      2020 Research-status Report
    • Peer Reviewed
  • [Journal Article] 感性指標化技術によるテクスチャの質感制御2020

    • Author(s)
      飛谷謙介、山﨑陽一、長田典子
    • Journal Title

      光アライアンス

      Volume: 31 Pages: 27-33

    • NAID

      40022424030

    • Related Report
      2020 Research-status Report
  • [Journal Article] スタイル特徴を利用したDNNによる印象推定に寄与する画像領域の可視化2020

    • Author(s)
      飛谷謙介、谷伊織、橋本翔、長田典子
    • Journal Title

      画像ラボ

      Volume: 31 Pages: 38-44

    • Related Report
      2020 Research-status Report
  • [Journal Article] Neural Style Featureを用いた衣服の柄における印象推定モデルの構築2019

    • Author(s)
      飛谷謙介・谷伊織・谿雄祐・長田典子・森田修史
    • Journal Title

      画像ラボ

      Volume: 30 Pages: 21-29

    • Related Report
      2019 Research-status Report
  • [Presentation] Textile-GAN:敵対的生成ネットワークを用いた織柄のテクスチャ生成 ~ 感性的質感に基づくスーツの柄の印象推定モデルへの応用 ~. 信学技報, 121(179), MVE2021-11, 19-202021

    • Author(s)
      津村瑛輝, 谷伊織, 飛谷謙介, 長田典子
    • Organizer
      信学技報, 121(179), MVE2021-11, 19-20
    • Related Report
      2021 Annual Research Report
  • [Presentation] 教師なし学習を用いた BTF 予測モデルの構築とテクスチャ生成2021

    • Author(s)
      木村綜一朗, 飛谷謙介, 長田典子
    • Organizer
      精密工学会IAIP サマーセミナー2021, 30, 47-50
    • Related Report
      2021 Annual Research Report
  • [Presentation] Textile GAN-敵対的生成ネットワークを用いた感性的質感認知に基づくテクスチャ生成-2021

    • Author(s)
      谷口史果, 飛谷謙介, 長田典子
    • Organizer
      精密工学会IAIP サマーセミナー2021, 30, 53-54
    • Related Report
      2021 Annual Research Report
  • [Presentation] BTF prediction model using unsupervised learning2021

    • Author(s)
      Kimura S, Tobitani K, Nagata N
    • Organizer
      Computer Science & Information Technology (CS & IT), 12(5), 45-53
    • Related Report
      2021 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Impression estimation model for clothing patterns using neural style features2020

    • Author(s)
      Natsuki Sunda, Kensuke Tobitani, Iori Tani, Yusuke Tani, Noriko Nagata and Nobufumi Morita
    • Organizer
      HCI International 2020(HC!!2020)
    • Related Report
      2020 Research-status Report
    • Int'l Joint Research
  • [Presentation] Neural Style Featureを用いた感性モデルに基づくテクスチャ生成2020

    • Author(s)
      寸田菜月、谷伊織、飛谷謙介、竹本敦、谿雄祐、長田典子、森田修史
    • Organizer
      ビジョン技術の実利用ワークショップ (ViEW2020)
    • Related Report
      2020 Research-status Report
  • [Presentation] Neural Style Featureを用いた感性モデルに基づく質感表現2020

    • Author(s)
      寸田菜月、谷伊織、飛谷謙介、竹本敦、谿雄祐、長田典子、森田修史
    • Organizer
      電子情報通信学会メディアエクスペリエンス・バーチャル環境基礎研究会(MVE)
    • Related Report
      2020 Research-status Report
  • [Presentation] CNNのスタイル特徴と感性指標に基づく印象推定モデルと柄検索システム2018

    • Author(s)
      寸田菜月・飛谷謙介・竹本敦・谷伊織・谿雄祐・藤原大志・長田典子・森田修史
    • Organizer
      ビジョン技術の実利用ワークショップ (ViEW2018) 講演論文集
    • Related Report
      2018 Research-status Report
  • [Presentation] CNNのスタイル特徴と感性指標に基づく印象推定モデルと柄検索システム2018

    • Author(s)
      寸田菜月・飛谷謙介・竹本敦・谷伊織・谿雄祐・藤原大志・長田典子・森田修史
    • Organizer
      第20回日本感性工学会大会
    • Related Report
      2018 Research-status Report
  • [Presentation] Impression estimation model and pattern search system based on style features and Kansei metric2018

    • Author(s)
      Sunda, N., Tobitani, K., Takemoto, A., Tani, I., Tani, Y., Fujiwara, T., Nagata, N., & Morita, N.
    • Organizer
      24th ACM Symposium on Virtual Reality Software and Technology (VRST'18)
    • Related Report
      2018 Research-status Report
    • Int'l Joint Research
  • [Remarks] CNNのスタイル特徴と感性指標に基づく柄検索システム

    • URL

      https://ist.ksc.kwansei.ac.jp/~nagata/projects/CNN-style.html

    • Related Report
      2018 Research-status Report

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Published: 2018-04-23   Modified: 2023-12-25  

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