Image Partitioning Into Convex Polygons

Liuyun Duan, Florent Lafarge; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 3119-3127

Abstract


The over-segmentation of images into atomic regions has become a standard and powerful tool in Vision. Traditional superpixel methods, that operate at the pixel level, cannot directly capture the geometric information disseminated into the images. We propose an alternative to these methods by operating at the level of geometric shapes. Our algorithm partitions images into convex polygons. It presents several interesting properties in terms of geometric guarantees, region compactness and scalability. The overall strategy consists in building a Voronoi diagram that conforms to preliminarily detected line-segments, before homogenizing the partition by spatial point process distributed over the image gradient. Our method is particularly adapted to images with strong geometric signatures, typically man-made objects and environments. We show the potential of our approach with experiments on large-scale images and comparisons with state-of-the-art superpixel methods.

Related Material


[pdf]
[bibtex]
@InProceedings{Duan_2015_CVPR,
author = {Duan, Liuyun and Lafarge, Florent},
title = {Image Partitioning Into Convex Polygons},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}