Academy & Industry Research Collaboration Center (AIRCC)

Volume 9, Number 3, March 2019

Image Segmentation Based on Multiplex Networks and Super Pixels


Ivo S. M. de Oliveira1,2, Oscar A. C. Linares1, Ary H. M. de Oliveira3, Glenda M. Botelho3 and João Batista Neto1, 1Universidade de São Paulo, Brazil, 2Campus de Paraíso do Tocantins, Brazil and 3Universidade Federal do Tocantins, Palmas, Brazil


Despite the large number of techniques and applications in the field of image segmentation, it is still an open research field. A recent trend in image segmentation is the usage of graph theory. This work proposes an approach which combines community detection in multiplex networks, in which a layer represents a certain image feature, with super pixels. There are approaches for the segmentation of images of good quality that use a single feature or the combination of several features of the image forming a single graph for the detection of communities and the segmentation. However, with the use of multiplex networks it is possible to use more than one image feature without the need for mathematical operations that can lead to the loss of information of the image features during the generation of the graphs. Through the related experiments, presented in this work, it is possible to identify that such method can offer quality and robust segmentations


community detection; complex networks; image segmentation; multiplex networks; super pixels