Multi-level Graph Label Propagation for Image Segmentation

• Ivar Vargas Belizario USP
• João Batista Neto USP

Resumo

This article introduces a multi-level automatic image segmentation method based on graphs and Label Propagation (LP), originally proposed for the detection of communities in complex networks, namely MGLP. To reduce the number of graph nodes, a super-pixel strategy is employed, followed by the computation of color descriptors. Segmentation is achieved by a deterministic propagation of vertex labels at each level. Several experiments with real color images of the BSDS500 dataset were performed to evaluate the method. Our method outperforms related strategies in terms of segmentation quality and processing time. Considering the Covering metric for image segmentation quality, for example, MGLP outperforms LPCI-SP, its most similar counterpart, in 38.99%. In term of processing times, MGLP is 1.07 faster than LPCI-SP.