Automatic image segmentation based on label propagation

Autor(es)

Belizario, Ivar Vargas ^[1] ; Linares, Oscar Cuadros ^[1] ; Santo Batista Neto, Joao do Espirito ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Math & Comp Sci ICMC, Sao Carlos - Brazil

• Tipo de documento: Artigo Científico
• Fonte: IET IMAGE PROCESSING; v. 15, n. 11, p. 2532-2547, SEP 2021.
• Citações Web of Science: 0

Resumo

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)

• Processo FAPESP: 18/06074-0 - Content-Based Image Retrieval using Selective Visual Attention
• Beneficiário: Oscar Alonso Cuadros Linares
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 21/00360-3 - Desenvolvimento de software de apoio para consultas por similaridade em bases de dados de saúde
• Beneficiário: Ivar Vargas Belizario
• Modalidade de apoio: Bolsas no Brasil - Programa Capacitação - Treinamento Técnico