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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Learning-Based Symmetry Detection in Natural Images
verfasst von : Stavros Tsogkas, Iasonas Kokkinos
Erschienen in: Computer Vision – ECCV 2012
Verlag: Springer Berlin Heidelberg
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In this work we propose a learning-based approach to symmetry detection in natural images. We focus on ribbon-like structures, i.e. contours marking local and approximate reflection symmetry and make three contributions to improve their detection. First, we create and make publicly available a ground-truth dataset for this task by building on the Berkeley Segmentation Dataset. Second, we extract features representing multiple complementary cues, such as grayscale structure, color, texture, and spectral clustering information. Third, we use supervised learning to
learn
how to combine these cues, and employ MIL to accommodate the unknown scale and orientation of the symmetric structures. We systematically evaluate the performance contribution of each individual component in our pipeline, and demonstrate that overall we consistently improve upon results obtained using existing alternatives.