Multi-layer feature extractions for image classification - Knowledge from deep CNNs

Kazuya Ueki, Tetsunori Kobayashi

    研究成果: Conference contribution

    抜粋

    Recently, there has been considerable research into the application of deep learning to image recognition. Notably, deep convolutional neural networks (CNNs) have achieved excellent performance in a number of image classification tasks, compared with conventional methods based on techniques such as Bag-of-Features (BoF) using local descriptors. In this paper, to cultivate a better understanding of the structure of CNN, we focus on the characteristics of deep CNNs, and adapt them to SIFT+BoF-based methods to improve the classification accuracy. We introduce the multi-layer structure of CNNs into the classification pipeline of the BoF framework, and conduct experiments to confirm the effectiveness of this approach using a fine-grained visual categorization dataset. The results show that the average classification rate is improved from 52.4% to 69.8%.

    元の言語English
    ホスト出版物のタイトル2015 22nd International Conference on Systems, Signals and Image Processing - Proceedings of IWSSIP 2015
    出版者Institute of Electrical and Electronics Engineers Inc.
    ページ9-12
    ページ数4
    ISBN(印刷物)9781467383530
    DOI
    出版物ステータスPublished - 2015 10 30
    イベント22nd International Conference on Systems, Signals and Image Processing, IWSSIP 2015 - London, United Kingdom
    継続期間: 2015 9 102015 9 12

    Other

    Other22nd International Conference on Systems, Signals and Image Processing, IWSSIP 2015
    United Kingdom
    London
    期間15/9/1015/9/12

      フィンガープリント

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Computer Vision and Pattern Recognition
    • Signal Processing

    これを引用

    Ueki, K., & Kobayashi, T. (2015). Multi-layer feature extractions for image classification - Knowledge from deep CNNs. : 2015 22nd International Conference on Systems, Signals and Image Processing - Proceedings of IWSSIP 2015 (pp. 9-12). [7313925] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWSSIP.2015.7313925