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Local Mutual Information for Dissimilarity-Based Image Segmentation

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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 multi-angular remote sensing images highlight the robustness of the proposed approach.

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Authors and Affiliations

  1. Fraunhofer Institute for Industrial Mathematics (ITWM), Kaiserslautern, Germany

    Santiago Velasco-Forero

  2. European Commission, Joint Research Centre, Ispra (VA), Italy

    Lionel Gueguen & Pierre Soille

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  1. Lionel Gueguen
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  2. Santiago Velasco-Forero
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  3. Pierre Soille
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Correspondence to Lionel Gueguen.

Additional information

This research was funded by the JRC Specific Programme of European Commission’s Seventh Framework Programme for Research and Technological Development (FP7). It was undertaken under the work programme of the Geo-Spatial Information Analysis for Security and Stability action, Global Security and Crisis Management unit, Institute for the Protection and Security of the Citizen. This study was performed whilst L. Gueguen was with the Joint Research Centre of the European Commission.

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Gueguen, L., Velasco-Forero, S. & Soille, P. Local Mutual Information for Dissimilarity-Based Image Segmentation. J Math Imaging Vis 48, 625–644 (2014). https://doi.org/10.1007/s10851-013-0432-9

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