HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

Local Mutual Information for Dissimilarity-Based Image Segmentation

Abstract : Connective segmentation based on the definition of a dissimilarity measure on pairs of adjacent pixels is an appealing framework to develop new hierarchical segmentation methods. Usually, the dissimilarity is fully determined by the intensity values of the considered pair of adjacent pixels, so that it is independent of the values of the other image pixels. In this paper, we explore dissimilarity measures depending on the overall image content encapsulated in its local mutual information and show its invariance to information preserving transforms. This is investigated in the framework of the connective segmentation and constrained connectivity paradigms and leads to the concept of dependent connectivities. An efficient probability estimator based on depth functions is proposed to handle multi-dimensional images. Experiments conducted on hyper-spectral and multiangular remote sensing images highlight the robustness of the proposed approach
Document type :
Journal articles
Complete list of metadata

Cited literature [59 references]  Display  Hide  Download

Contributor : Santiago Velasco-Forero Connect in order to contact the contributor
Submitted on : Wednesday, January 28, 2015 - 4:57:15 PM
Last modification on : Wednesday, November 17, 2021 - 12:27:12 PM
Long-term archiving on: : Wednesday, April 29, 2015 - 10:20:57 AM


Local Mutual Information for d...
Files produced by the author(s)


  • HAL Id : hal-01110199, version 1


Lionel Gueguen, Santiago Velasco-Forero, Pierre Soille. Local Mutual Information for Dissimilarity-Based Image Segmentation. Journal of Mathematical Imaging and Vision, Springer Verlag, 2014, 48 (3), pp.625-644. ⟨hal-01110199⟩



Record views


Files downloads