Implementacion del algoritmo de modelos mixtos gaussianos para la mejora del agrupamiento espectral (Gaussian Mixture Models Implementation to Enhance Spectral Clustering)

José Rodolfo Vázquez Fuentes (rodo_hs10@hotmail.com)1, Irene Olaya Ayaquica Martínez (ayaquica@gmail.com)2, Daniel Alejandro Valdés Amaro (daniel.valdes@cs.buap.mx)2, Carlos Guillén Galván2


1BIC Consulting
2Benemérita Universidad Autónoma de Puebla

This paper appears in: Revista IEEE América Latina

Publication Date: March 2016
Volume: 14,   Issue: 3 
ISSN: 1548-0992


Abstract:
Nature variability has been studied for many years due to its importance in research areas such as Biology or Medicine. In order to characterize such variability, different methods have been used. Since the shape is one of the most important features of human perception, it is natural to assess the variation using shape models. Moreover, one of the most important activities in data analysis is clustering, meaning the task of grouping a set of objects in such a way that objects in the same group are more similar than objects in different groups. This paper presents a modification to the spectral clustering methodology, introduced by Valdes-Amaro and Bhalerao in 2009, using the Gaussian Mixture Models as a replacement for K-Means. In addition, a new shape descriptor is proposed to use it in the aforementioned methodology, called angular magnitude. Results are presented over different sets of shapes from natural and artificial objects, along with two different measurements to evaluate them quantitatively.

Index Terms:
shape, shape descriptors, spectral clustering, diffusion maps, brain contours   


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