Classification-driven Active Contour for Dress Segmentation - Archive ouverte HAL Access content directly
Conference Papers Year : 2016

Classification-driven Active Contour for Dress Segmentation


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.
Fichier principal
Vignette du fichier
yang16classification-driven.pdf (2.74 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-02435274 , version 1 (10-01-2020)



Lixuan Yang, Helena Rodriguez, Michel Crucianu, Marin Ferecatu. Classification-driven Active Contour for Dress Segmentation. International Conference on Computer Vision Theory and Applications, Feb 2016, Rome, France. pp.22-29, ⟨10.5220/0005721000220029⟩. ⟨hal-02435274⟩
48 View
71 Download



Gmail Mastodon Facebook X LinkedIn More