Full Text:   <3368>

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CLC number: TP391.7

On-line Access: 2016-01-05

Received: 2015-05-27

Revision Accepted: 2015-09-16

Crosschecked: 2015-10-21

Cited: 1

Clicked: 5900

Citations:  Bibtex RefMan EndNote GB/T7714


Hao Xie


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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.1 P.32-40


Image meshing via hierarchical optimization

Author(s):  Hao Xie, Ruo-feng Tong

Affiliation(s):  Institute of Artificial Intelligence, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   xiehao@zju.edu.cn, trf@zju.edu.cn

Key Words:  Image meshing, Hierarchical optimization, Convexification

Hao Xie, Ruo-feng Tong. Image meshing via hierarchical optimization[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(1): 32-40.

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DOI - 10.1631/FITEE.1500171

Vector graphic, as a kind of geometric representation of raster images, has many advantages, e.g., definition independence and editing facility. A popular way to convert raster images into vector graphics is image meshing, the aim of which is to find a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to find a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to finer ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images.

This paper proposes a new approach to image meshing as a way to compactly represent images. The major new idea is to use a hierarchical optimization with the combined color and location to make the problem more tractable. The experimental results demonstrate that the method produces improved results over state of the art. Overall, it is a nice paper with soild technical contribution and interesting results.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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