Segmentation of large images based on super‐pixels and community detection in graphs. Issue 12 (25th October 2017)

• Record Type: Journal Article
• Authors: Linares, Oscar A.C.; Botelho, Glenda Michele; Rodrigues, Francisco Aparecido; Neto, João Batista
• Is Part Of: IET image processing. Volume 11:Issue 12(2017)
• Page Start: 1219
• Page End: 1228
• Publication Date: 2017-10-25
• Subjects: image segmentation -- graph theory -- object detection; contour‐based method -- graph‐based approach -- spectral graph partition -- community detection algorithms -- super‐pixel pre‐segmentation step -- medical diagnosis -- machine learning -- image segmentation; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.0072
• Languages: English
• ISSNs: 1751-9659

Abstract

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.