Abstract
Image segmentation is generally performed in a “one image, one algorithm” paradigm. However, it is sometimes required to consider several images of a same scene, or to carry out several (or several occurrences of a same) algorithm(s) to fully capture relevant information. To solve the induced segmentation fusion issues, various strategies have been already investigated for allowing a consensus between several segmentation outputs. This article proposes a contribution to segmentation fusion, with a specific focus on the “n images” part of the paradigm. Its main originality is to act on the segmentation research space, i.e., to work at an earlier stage than standard segmentation fusion approaches. To this end, an algorithmic framework is developed to build a binary partition tree in a collaborative fashion, from several images, thus allowing to obtain a unified hierarchical segmentation space. This framework is, in particular, designed to embed consensus policies inherited from the machine learning domain. Application examples proposed in remote sensing emphasise the potential usefulness of our approach for satellite image processing.
This research was partially funded by the French Agence Nationale de la Recherche (Grant Agreements ANR-10-BLAN-0205 and ANR-12-MONU-0001).
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Randrianasoa, J.F., Kurtz, C., Desjardin, É., Passat, N. (2015). Multi-image Segmentation: A Collaborative Approach Based on Binary Partition Trees. In: Benediktsson, J., Chanussot, J., Najman, L., Talbot, H. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2015. Lecture Notes in Computer Science(), vol 9082. Springer, Cham. https://doi.org/10.1007/978-3-319-18720-4_22
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DOI: https://doi.org/10.1007/978-3-319-18720-4_22
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