Open AccessSegmentation of large images based on super‐pixels and community detection in graphs
Author(s) -
Linares Oscar A.C.,
Botelho Glenda Michele,
Rodrigues Francisco Aparecido,
Neto João Batista
Publication year - 2017
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/iet-ipr.2016.0072
Subject(s) - segmentation , pixel , computer science , artificial intelligence , image segmentation , segmentation based object categorization , scale space segmentation , rendering (computer graphics) , graph partition , pattern recognition (psychology) , graph , minimum spanning tree based segmentation , cut , computer vision , theoretical computer science
Image segmentation has many applications which range from machine learning to medical diagnosis. In this study, the authors propose a framework for the segmentation of images based on super‐pixels and algorithms for community identification in graphs. The super‐pixel pre‐segmentation step reduces the number of nodes in the graph, rendering the method the ability to process large images. Moreover, community detection algorithms provide more accurate segmentation than traditional approaches based on spectral graph partition. The authors also compared their method with two algorithms: (i) the graph‐based approach by Felzenszwalb and Huttenlocher and (ii) the contour‐based method by Arbelaez. Results have shown that their method provides more precise segmentation and is faster than both of them.