| Full text | |
| Author(s): |
Total Authors: 3
|
| Affiliation: | [1] Univ Sao Paulo, Inst Math & Comp Sci ICMC, Sao Carlos - Brazil
Total Affiliations: 1
|
| Document type: | Journal article |
| Source: | IET IMAGE PROCESSING; v. 15, n. 11, p. 2532-2547, SEP 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
This article introduces an automatic approach for the segmentation of coloured natural scene images based on graphs and the propagation of labels originally designed for communities detection in complex networks. Images are initially pre-segmented with super-pixels, followed by feature extraction using colour information of each super-pixels. The resulting graph consists of vertices which represent super-pixels, whereas the edge weights are a measure of similarity between super-pixels. The resulting segmentation corresponds to the propagation of labels among the vertices. In this article, three strategies for propagating labels have been formulated: (i) iterative propagation (ILP), (ii) recursive propagation (RLP) and (iii) a weighted recursive propagation (WRLP). The experiments have shown that the proposed methods, when compared to other state-of-the-art methods, produce better results in terms of segmentation quality and processing time. (AU) | |
| FAPESP's process: | 18/06074-0 - Recuperação de Imagens por Conteúdo Utilizando Atenção Visual Seletiva |
| Grantee: | Oscar Alonso Cuadros Linares |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 21/00360-3 - Development of software to support similarity queries in healthcare databases |
| Grantee: | Ivar Vargas Belizario |
| Support Opportunities: | Scholarships in Brazil - Technical Training Program - Technical Training |