Can the Use of nonlinear Color Metrics systematically improve Segmentation?

Luís Eduardo Ramos de Carvalho; Sylvio Luiz Mantelli Neto; Eros Comunello; Antonio Carlos Sobieranski; Aldo von Wangenheim

doi:10.22456/2175-2745.79885

Authors

Luís Eduardo Ramos de Carvalho Graduate Program in Computer Science - Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil. National Brazilian Institute for Digital Convergence - Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil. http://orcid.org/0000-0003-2052-1279
Sylvio Luiz Mantelli Neto Brazilian Institute for Space Research - INPE, São José dos Campos, São Paulo, Brazil. National Brazilian Institute for Digital Convergence - Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil.
Eros Comunello University of Itajai Valley - Univali, Itajaí, Santa Catarina, Brazil. National Brazilian Institute for Digital Convergence - Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil.
Antonio Carlos Sobieranski Department of Computing - Federal University of Santa Catarina, Ararangua, Brazil. National Brazilian Institute for Digital Convergence - Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil.
Aldo von Wangenheim Graduate Program in Computer Science - Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil. National Brazilian Institute for Digital Convergence - Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil.

DOI:

https://doi.org/10.22456/2175-2745.79885

Keywords:

Image Segmentation, Nonlinear Color Metrics, Polynomial Mahalanobis Distance, Split and Merge, Variational Methods, Graph-Based Segmentation.

Abstract

Image segmentation is a procedure where an image is split into its constituent parts, according to some criterion. In the literature, there are different well-known approaches for segmentation, such as clustering, thresholding, graph theory and region growing. Such approaches, additionally, can be combined with color distance metrics, playing an important role for color similarity computation. Aiming to investigate general approaches able to enhance the performance of segmentation methods, this work presents an empirical study of the effect of a nonlinear color metric on segmentation procedures. For this purpose, three algorithms were chosen: Mumford-Shah, Color Structure Code and Felzenszwalb and Huttenlocher Segmentation. The color similarity metric employed by these algorithms (L2-norm) was replaced by the Polynomial Mahalanobis Distance. This metric is an extension of the statistical Mahalanobis Distance used to measure the distance between coordinates and distribution centers. An evaluation based upon automated comparison of segmentation results against ground truths from the Berkeley Dataset was performed. All three segmentation approaches were compared to their traditional implementations, against each other and also to a large set of other segmentation methods. The statistical analysis performed has indicated a systematic improvement of segmentation results for all three segmentation approaches when the nonlinear metric was employed.

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