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An information theoretic framework for image segmentation

In this paper, an information theoretic framework for image segmentation is presented. This approach is based on the information channel that goes from the image intensity histogram to the regions of the partitioned image. It allows us to define a new family of segmentation methods which maximize the mutual information of the channel. Firstly, a greedy top-down algorithm which partitions an image into homogeneous regions is introduced. Secondly, a histogram quantization algorithm which clusters color bins in a greedy bottom-up way is defined. Finally, the resulting regions in the partitioning algorithm can optionally be merged using the quantized histogram

IEEE

Author: Rigau Vilalta, Jaume
Feixas Feixas, Miquel
Sbert, Mateu
Abstract: In this paper, an information theoretic framework for image segmentation is presented. This approach is based on the information channel that goes from the image intensity histogram to the regions of the partitioned image. It allows us to define a new family of segmentation methods which maximize the mutual information of the channel. Firstly, a greedy top-down algorithm which partitions an image into homogeneous regions is introduced. Secondly, a histogram quantization algorithm which clusters color bins in a greedy bottom-up way is defined. Finally, the resulting regions in the partitioning algorithm can optionally be merged using the quantized histogram
Document access: http://hdl.handle.net/2072/94970
Language: eng
Publisher: IEEE
Rights: Tots els drets reservats
Subject: Algorismes computacionals
Imatges -- Segmentació
Imatges -- Processament
Imaging segmentation
Computer algorithms
Image processing
Title: An information theoretic framework for image segmentation
Type: info:eu-repo/semantics/article
Repository: Recercat

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