Learning-Based Symmetry Detection in Natural Images

Abstract : In this work we propose a learning-based approach to sym- metry 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 build- ing 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 super- vised learning to learn how to combine these cues, and employ MIL to accommodate the unknown scale and orientation of the symmetric struc- tures. 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.
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Communication dans un congrès
ECCV - 12th European Conference on Computer Vision, Oct 2012, Florence, Italy. pp.41-54, 2012
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Stavros Tsogkas, Iasonas Kokkinos. Learning-Based Symmetry Detection in Natural Images. ECCV - 12th European Conference on Computer Vision, Oct 2012, Florence, Italy. pp.41-54, 2012. 〈hal-00856535〉

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