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Bardera i Reig, Antoni
Rigau Vilalta, Jaume Boada, Imma Feixas Feixas, Miquel Sbert, Mateu 

2018 June 5  
In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image segmentation algorithms based on a hard version of the information bottleneck method are presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. First, we introduce a splitandmerge algorithm based on the definition of an information channel between a set of regions (input) of the image and the intensity histogram bins (output). From this channel, the maximization of the mutual information gain is used to optimize the image partitioning. Then, the merging process of the regions obtained in the previous phase is carried out by minimizing the loss of mutual information. From the inversion of the above channel, we also present a new histogram clustering algorithm based on the minimization of the mutual information loss, where now the input variable represents the histogram bins and the output is given by the set of regions obtained from the above splitandmerge algorithm. Finally, we introduce two new clustering algorithms which show how the information bottleneck method can be applied to the registration channel obtained when two multimodal images are correctly aligned. Different experiments on 2D and 3D images show the behavior of the proposed algorithms  
http://hdl.handle.net/2072/320693  
eng  
IEEE  
Tots els drets reservats  
Algorismes computacionals
Imatges  Processament Imatges  SegmentaciÃ³ Imatgeria tridimensional Computer algorithms Image processing Imaging segmentation Threedimensional imaging 

Image Segmentation Using Information Bottleneck Method  
info:eurepo/semantics/article  
Recercat 