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.
Carvalho, Marco A. G. de
Pedronette, Daniel C. G. [UNESP]
Martins, Paulo S.
IEEE

Título da Revista

ISSN da Revista

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Editor

Ieee

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.

Descrição

Palavras-chave

Image segmentation, Watershed transform, Graph partitioning, Normalized cut, Unsupervised distance learning

Como citar

2014 Ieee Southwest Symposium On Image Analysis And Interpretation (ssiai 2014). New York: Ieee, p. 153-156, 2014.