Climbing: A unified approach for global constraints on hierarchical segmentation

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Abstract

The paper deals with global constraints for hierarchical segmentations. The proposed framework associates, with an input image, a hierarchy of segmentations and an energy, and the subsequent optimization problem. It is the first paper that compiles the different global constraints and unifies them as Climbing energies. The transition from global optimization to local optimization is attained by the h-increasingness property, which allows to compare parent and child partition energies in hierarchies. The laws of composition of such energies are established and examples are given over the Berkeley Dataset for colour and texture segmentation. © 2012 Springer-Verlag.

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APA

Kiran, B. R., Serra, J., & Cousty, J. (2012). Climbing: A unified approach for global constraints on hierarchical segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7585 LNCS, pp. 324–334). Springer Verlag. https://doi.org/10.1007/978-3-642-33885-4_33

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