Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14918
Title: Variational and PCA based natural image segmentation
Authors: Han, Y
Feng, XC
Baciu, G 
Keywords: Image segmentation
Iterative reweighting
Principal component analysis
Region competition
Variable splitting
Issue Date: 2013
Publisher: Elsevier
Source: Pattern recognition, 2013, v. 46, no. 7, p. 1971-1984 How to cite?
Journal: Pattern recognition 
Abstract: This paper introduces a novel variational segmentation method within the fuzzy framework, which solves the problem of segmenting multi-region color-scale images of natural scenes. We call this kind of images as natural images. The advantages of our segmentation method are: (1) by introducing the PCA descriptors, our segmentation model can partition color-texture images better than classical variational-based segmentation models, (2) to preserve geometrical structure of each fuzzy membership function, we propose a nonconvex regularization term in our model, (3) to solve our segmentation model more efficiently, we design a fast iteration algorithm in which we integrate the augmented Lagrange multiplier method and the iterative reweighting. We conduct comprehensive experiments to measure the segmentation performance of our model in terms of visual evaluation, and we also demonstrate the efficiency of the corresponding algorithm in terms of a variety of quantitative indices. The proposed model achieves better segmentation results compared with some other well-known models, such as the level-set model and the fuzzy region competition model, while the proposed algorithm is much more efficient than the algorithm of the state-of-the-art natural image segmentation model.
URI: http://hdl.handle.net/10397/14918
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2012.12.002
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