Classification-driven Active Contour for Dress Segmentation

Lixuan Yang, Helena Rodriguez, Michel Crucianu, Marin Ferecatu


In this work we propose a a dedicated object extractor for dress segmentation in fashion images by combining local information with a prior learning. First, a person detector is applied to localize sites in the image that are likely to contain the object. Then, an intra-image two-stage learning process is developed to roughly separate foreground pixels from the background. Finally, the object is finely segmented by employing an active contour algorithm that takes into account the previous segmentation and injects specific knowledge about local curvature in the energy function. The method is validated on a database of manually segmented images. We show examples of both successful segmentation and difficult cases. We quantitatively analyze each component and compare with the well-known GrabCut foreground extraction method.


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Paper Citation

in Harvard Style

Yang L., Rodriguez H., Crucianu M. and Ferecatu M. (2016). Classification-driven Active Contour for Dress Segmentation . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 22-29. DOI: 10.5220/0005721000220029

in Bibtex Style

author={Lixuan Yang and Helena Rodriguez and Michel Crucianu and Marin Ferecatu},
title={Classification-driven Active Contour for Dress Segmentation},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},

in EndNote Style

JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - Classification-driven Active Contour for Dress Segmentation
SN - 978-989-758-175-5
AU - Yang L.
AU - Rodriguez H.
AU - Crucianu M.
AU - Ferecatu M.
PY - 2016
SP - 22
EP - 29
DO - 10.5220/0005721000220029