Bagging Stochastic Watershed on Natural Color Image Segmentation

International audience The stochastic watershed is a probabilistic segmentation ap-proach which estimates the probability density of contours of the image from a given gradient. In complex images, the stochastic watershed can enhance insignificant contours. To partially address this drawback, we int...

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Bibliographic Details
Main Authors: Franchi, Gianni, Angulo, Jesus
Other Authors: Centre de Morphologie Mathématique (CMM), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)
Format: Conference Object
Language:English
Published: HAL CCSD 2015
Subjects:
Berkeley segmentation database
unsupervised image segmentation
stochastic watershed
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
Iceland
Online Access:https://hal.science/hal-01104256
https://hal.science/hal-01104256/document
https://hal.science/hal-01104256/file/Bagging_Stochastic_Watershed_FRANCHI_ANGULO_V2.pdf
https://doi.org/10.1007/978-3-319-18720-4_36
Description
Summary:International audience The stochastic watershed is a probabilistic segmentation ap-proach which estimates the probability density of contours of the image from a given gradient. In complex images, the stochastic watershed can enhance insignificant contours. To partially address this drawback, we introduce here a fully unsupervised multi-scale approach including bag-ging. Re-sampling and bagging is a classical stochastic approach to im-prove the estimation. We have assessed the performance, and compared to other version of stochastic watershed, using the Berkeley segmentation database.

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