Open AccessAutomatic image segmentation based on label propagation
Author(s) -
Belizario Ivar Vargas,
Linares Oscar Cuadros,
Neto João do Espirito Santo Batista
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12242
Subject(s) - pixel , segmentation , artificial intelligence , computer science , image segmentation , pattern recognition (psychology) , random walker algorithm , similarity (geometry) , feature (linguistics) , graph , computer vision , feature extraction , enhanced data rates for gsm evolution , image (mathematics) , theoretical computer science , linguistics , philosophy
This article introduces an automatic approach for the segmentation of coloured natural scene images based on graphs and the propagation of labels originally designed for communities detection in complex networks. Images are initially pre‐segmented with super‐pixels, followed by feature extraction using colour information of each super‐pixels. The resulting graph consists of vertices which represent super‐pixels, whereas the edge weights are a measure of similarity between super‐pixels. The resulting segmentation corresponds to the propagation of labels among the vertices. In this article, three strategies for propagating labels have been formulated: (i) iterative propagation (ILP), (ii) recursive propagation (RLP) and (iii) a weighted recursive propagation (WRLP). The experiments have shown that the proposed methods, when compared to other state‐of‐the‐art methods, produce better results in terms of segmentation quality and processing time.