Scale-Based Gaussian Coverings: Combining Intra and Inter Mixture Models in Image Segmentation

Entropy

Fionn Murtagh¹  Pedro Contreras² 

[1] Science Foundation Ireland, Wilton Park House, Wilton Place, Dublin 2, Ireland;Department of Computer Science, Royal Holloway University of London, Egham TW20 0EX, UK; E-Mail:

关键词: image segmentation;    clustering;    model selection;    minimum description length;    Bayes factor;    Rényi entropy;    Shannon entropy;   

DOI  :  10.3390/e11030513

来源: mdpi

【 摘 要 】

By a “covering” we mean a Gaussian mixture model fit to observed data. Approximations of the Bayes factor can be availed of to judge model fit to the data within a given Gaussian mixture model. Between families of Gaussian mixture models, we propose the Rényi quadratic entropy as an excellent and tractable model comparison framework. We exemplify this using the segmentation of an MRI image volume, based (1) on a direct Gaussian mixture model applied to the marginal distribution function, and (2) Gaussian model fit through k-means applied to the 4D multivalued image volume furnished by the wavelet transform. Visual preference for one model over another is not immediate. The Rényi quadratic entropy allows us to show clearly that one of these modelings is superior to the other.

【 授权许可】

CC BY   
© 2009 by the authors; licensee MDPI, Basel, Switzerland.