Watersheds for Semi-Supervised Classification

Abstract : Watershed technique from mathematical morphology (MM) is one of the most widely used operators for image segmentation. Recently watersheds are adapted to edge weighted graphs, allowing for wider applicability. However, a few questions remain to be answered-(a) How do the boundaries of the watershed operator behave? (b) Which loss function does the watershed operator optimize? (c) How does watershed operator relate with existing ideas from machine learning. In this article, a framework is developed, which allows one to answer these questions. This is achieved by generalizing the maximum margin principle to maximum margin partition and proposing a generic solution, MORPHMEDIAN, resulting in the maximum margin principle. It is then shown that watersheds form a particular class of MORPHMEDIAN classifiers. Using the ensemble technique, watersheds are also extended to ensemble watersheds. These techniques are compared with relevant methods from literature and it is shown that watersheds perform better than SVM on some datasets, and ensemble watersheds usually outperform random forest classifiers.
Document type :
Journal articles
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01977705
Contributor : Aditya Challa <>
Submitted on : Tuesday, March 12, 2019 - 12:17:49 PM
Last modification on : Thursday, April 11, 2019 - 4:44:02 AM

File

MorphologyAndDataAnalysis_Fina...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01977705, version 2

Collections

Citation

Aditya Challa, Sravan Danda, B Daya Sagar, Laurent Najman. Watersheds for Semi-Supervised Classification. IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, In press. ⟨hal-01977705v2⟩

Share

Metrics

Record views

18

Files downloads

34