Paper Info
Title
A Novel Post-Processing Method Based on a Weighted Composite Filter for Enhancing Semantic Segmentation Results.
Abstract
Image semantic segmentation is one of the key problems in computer vision. Despite the enormous advances in applications, almost all the image semantic segmentation algorithms fail to achieve satisfactory segmentation results due to lack of sensitivity to details, or difficulty in evaluating the global similarity of pixels, or both. Posting-processing enhancement methods, as the outstandingly crucial means to ameliorate the above-mentioned inherent flaws of algorithms, are almost based on conditional random fields (CRFs). Inspired by CRFs, this paper proposes a novel post-processing enhancement framework with theoretical simplicity from the perspective of filtering, and a new weighted composite filter (WCF) is designed to enhance the segmentation masks in a unified framework. First, by adjusting the weight ratio, the WCF is decomposed into a local part and a global part. Secondly, a guided image filter is designed as the local filter, which can restore boundary information to present necessary details. Moreover, a minimum spanning tree (MST)-based filter is designed as the global filter to provide a natural measure of global pixel similarity for image matching. Thirdly, a unified post-processing enhancement framework, including selection and normalization, WCF and argmax, is designed. Finally, the effectiveness and superiority of the proposed method for enhancement, as well as its range of applications, are verified through experiments.
Year
DOI
Venue
2020
10.3390/s20195500
SENSORS
Keywords
DocType
Volume
image semantic segmentation,post-processing enhancement,weighted composite filter (WCF),guided image filter,minimum spanning tree (MST)-based filter
Journal
20
Issue
ISSN
Citations 
19
1424-8220
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Xin Cheng100.34
Hua-shan Liu273.22