کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
528663 869593 2014 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Iterative transductive learning for automatic image segmentation and matting with RGB-D data
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Iterative transductive learning for automatic image segmentation and matting with RGB-D data
چکیده انگلیسی


• Iterative depth refinement and bi-layer classification is designed for segmentation.
• We model the matting technique as an iterative transductive learning problem.
• Coefficients between constraints and objective functions are adjusted adaptively.
• A novel way to form the matting Laplacian matrix is proposed.

In this paper, we propose a fully automatic image segmentation and matting approach with RGB-Depth (RGB-D) data based on iterative transductive learning. The algorithm consists of two key elements: robust hard segmentation for trimap generation, and iterative transductive learning based image matting. The hard segmentation step is formulated as a Maximum A Posterior (MAP) estimation problem, where we iteratively perform depth refinement and bi-layer classification to achieve optimal results. For image matting, we propose a transductive learning algorithm that iteratively adjusts the weights between the objective function and the constraints, overcoming common issues such as over-smoothness in existing methods. In addition, we present a new way to form the Laplacian matrix in transductive learning by ranking similarities of neighboring pixels, which is essential to efficient and accurate matting. Extensive experimental results are reported to demonstrate the state-of-the-art performance of our method both subjectively and quantitatively.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Visual Communication and Image Representation - Volume 25, Issue 5, July 2014, Pages 1031–1043
نویسندگان
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