Paper Info
Title
A Combined Approach Based on Fuzzy Classification and Contextual Region Growing to Image Segmentation
Abstract
We present in this paper an image segmentation approach that combines a fuzzy semantic region classification and a context based region-growing. Input image is first over-segmented. Then, prior domain knowledge is used to perform a fuzzy classification of these regions to provide a fuzzy semantic labeling. This allows the proposed approach to operate at high level instead of using low-level features and consequently to remedy to the problem of the semantic gap. Each oversegmented region is represented by a vector giving its corresponding membership degrees to the different thematic labels and the whole image is therefore represented by a Regions Partition Matrix. The segmentation is achieved on this matrix instead of the image pixels through two main phases: focusing and propagation. The focusing aims at selecting seeds regions from which information propagation will be performed. The propagation phase allows to spread toward others regions and using fuzzy contextual information the needed knowledge ensuring the semantic segmentation. An application of the proposed approach on mammograms shows promising results.
Year
DOI
Venue
2016
10.1109/CGiV.2016.41
2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV)
Keywords
Field
DocType
Image Segmentation,Fuzzy Classification,Region-growing,Context Information,Contextual Region-growing
Scale-space segmentation,Fuzzy classification,Pattern recognition,Image texture,Computer science,Segmentation-based object categorization,Image segmentation,Region growing,Artificial intelligence,Contextual image classification,Minimum spanning tree-based segmentation,Machine learning
Journal
Volume
Citations 
PageRank 
abs/1608.02373
0
0.34
References 
Authors
6
4
Name
Order
Citations
PageRank
Mahaman Sani Chaibou100.34
Karim Kalti2208.58
Soulaiman Bassel300.68
Mohamed Ali Mahjoub48332.74