NDLI: Segmentation of large images based on super-pixels and community detection in graphs

Content Provider	IET Digital Library
Author	Linares, Oscar A. C. Botelho, Glenda Michele Rodrigues, Francisco Aparecido Neto, João Batista
Abstract	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.
Starting Page	1219
Ending Page	1228
Page Count	10
ISSN	17519659
Volume Number	11
e-ISSN	17519667
Issue Number	Issue 12, Dec (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/11/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.0072
Journal	IET Image Processing
Publisher Date	2017-03-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Combinatorial Mathematics Community Detection Algorithm Computer Vision And Image Processing Technique Contour-based Method Graph Theory Graph-based Approach Image Segmentation Machine Learning Medical Diagnosis Object Detection Optical, Image And Video Signal Processing Spectral Graph Partition Super-pixel Pre-segmentation Step
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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