Autor(es):
Pinto, Tiago W. ; De Carvalho, Marco A. G. ; Pedronette, Daniel C. G. [UNESP] ; Martins, Paulo S.
Data: 2018
Identificador Persistente: http://hdl.handle.net/11449/171597
Origem: Oasisbr
Assunto(s): graph partitioning; image segmentation; normalized cut; unsupervised distance learning; watershed transform; graph partitioning; graph partitioning; image segmentation; image segmentation; normalized cut; normalized cut; unsupervised distance learning; unsupervised distance learning; watershed transform; watershed transform
Descrição
Made available in DSpace on 2018-12-11T16:56:09Z (GMT). No. of bitstreams: 0 Previous issue date: 2014-01-01
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.
School of Technology, UNICAMP, Limeira - 13484-332, São Paulo
Department of Statistics, Applied Mathematics and Computing, UNESP, Rio Claro - 13506-900, São Paulo
Department of Statistics, Applied Mathematics and Computing, UNESP, Rio Claro - 13506-900, São Paulo
