Natural Image Matting with Membership Propagation

• Item type: Trans(1)
• 公開日: 2009-01-30
• タイトル: Natural Image Matting with Membership Propagation
• タイトル / 言語: en
• タイトル: Natural Image Matting with Membership Propagation
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: Special Issue on ACCV2007 Regular Paper
• 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
• 著者所属: Kyoto Institute of Technology
• 著者所属: Kyushu University
• 著者所属(英): en
• 著者所属(英): Kyoto Institute of Technology
• 著者所属(英): en
• 著者所属(英): Kyushu University
• 著者名: Weiwei, Du; Kiichi, Urahama
• 著者名(英): Weiwei, Du; Kiichi, Urahama
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: We present a semi-supervised technique of object extraction for natural image matting. At first, we present a novel unsupervised graph-spectral algorithm for extraction of homogeneous regions in an image. We next derive a semi-supervised scheme from this unsupervised algorithm. In our method, it is sufficient for users to draw strokes only in one of object and background regions. The semi-supervised optimization problem is solved with an iterative method where memberships are propagated from strokes to their surroundings. We suggest a guideline for placement of strokes by exploiting the same iterative solution process in the unsupervised algorithm. We project the color vectors with the linear discriminant analysis to improve the color discriminability and speed up the convergence of the iterative method. Performance of the proposed method is examined for some images and the results are compared with other methods and ground truth mattes.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: We present a semi-supervised technique of object extraction for natural image matting. At first, we present a novel unsupervised graph-spectral algorithm for extraction of homogeneous regions in an image. We next derive a semi-supervised scheme from this unsupervised algorithm. In our method, it is sufficient for users to draw strokes only in one of object and background regions. The semi-supervised optimization problem is solved with an iterative method where memberships are propagated from strokes to their surroundings. We suggest a guideline for placement of strokes by exploiting the same iterative solution process in the unsupervised algorithm. We project the color vectors with the linear discriminant analysis to improve the color discriminability and speed up the convergence of the iterative method. Performance of the proposed method is examined for some images and the results are compared with other methods and ground truth mattes.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12394973
• 書誌情報: IPSJ Transactions on Computer Vision and Applications （CVA）; 巻 1, p. 3-11, 発行日 2009-01-30
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6695
• 出版者 / 言語: ja
• 出版者: 情報処理学会