Abstract
In recent work, several popular segmentation methods have been unified as energy minimization on a graph. In other work, supervised learning methods have been generalized from predicting labels to predicting structured, graph-like objects. A recent contribution to this second area showed how the Rand Index could be directly minimized when using Connected Components as a segmentation method. We build on this work and present an efficient mini-batch learning method for Connected Component segmentation and also show how it can be generalized to the Watershed Cuts segmentation method. We present initial results applying these new contributions to image segmentation problems in materials microscopy and discuss challenges and future directions.
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Porter, R., Oyen, D., Zimmer, B.G. (2015). Learning Watershed Cuts Energy Functions. 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_42
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DOI: https://doi.org/10.1007/978-3-319-18720-4_42
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18719-8
Online ISBN: 978-3-319-18720-4
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