Title
A Fuzzy Rule-based System using a Patch-based Approach for Semantic Segmentation in Floor Plans
Abstract
Semantic segmentation models help with the extraction of information from images. Currently, Convolutional Neural Networks (CNNs) are the state of the art for performing such tasks but the interpretability in their predictions is low. Previous work had proposed the use of Fuzzy Logic Rule-based systems (FRBS) as an explainable AI classifier of pixels for segmentation of images. In this paper, we extend that approach by using the similarity between image patches as context information for our model. The type-1 FRBS that uses the proposed set of context information features reaches an average Intersection over Union (IoU) value 3.51% higher than the type-1 FRBS using colour information. The difference in average IoU is significant due to the importance of colour in the testing images and the already high IoU value from the type-1 FRBS using colour.
Year
DOI
Venue
2021
10.1109/FUZZ45933.2021.9494427
2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Keywords
DocType
ISSN
semantic segmentation,floor plans,patch-based segmentation,fuzzy logic,rule-based systems
Conference
1544-5615
ISBN
Citations 
PageRank 
978-1-6654-4408-8
1
0.35
References 
Authors
0
5
Name
Order
Citations
PageRank
Hugo Leon-Garza110.69
Hani Hagras21747129.26
Anasol Peña-Ríos310.35
Anthony Conway410.35
Gilbert Owusu510.35