Image segmentation through combined methods: Watershed transform, unsupervised distance learning and normalized cut
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Data
2014-01-01
Autores
Pinto, Tiago W.
De Carvalho, Marco A. G.
Pedronette, Daniel C. G. [UNESP]
Martins, Paulo S.
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Resumo
Research on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance. © 2014 IEEE.
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graph partitioning, image segmentation, normalized cut, unsupervised distance learning, watershed transform
Como citar
Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, p. 153-156.