Towards a Simple and Efficient Object-based Superpixel Delineation Framework

Autor(es)

Belem, Felipe C. ; Perret, Benjamin ; Cousty, Jean ; Guimaraes, Silvio J. F. ; Falcao, Alexandre X. ; IEEE Comp Soc

• Tipo de documento: Artigo Científico
• Fonte: 2021 34TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2021); v. N/A, p. 8-pg., 2021-01-01.

Resumo

Superpixel segmentation methods are widely used in computer vision applications due to their properties in border delineation. These methods do not usually take into account any prior object information. Although there are a few exceptions, such methods significantly rely on the quality of the object information provided and present high computational cost in most practical cases. Inspired by such approaches, we propose Object-based Dynamic and Iterative Spanning Forest (ODISF), a novel object-based superpixel segmentation framework to effectively exploit prior object information while being robust to the quality of that information. ODISF consists of three independent steps: (i) seed oversampling; (ii) dynamic path-based superpixel generation; and (iii) object-based seed removal. After (i), steps (ii) and (iii) are repeated until the desired number of superpixels is finally reached. Experimental results show that ODISF can surpass state-of-the-art methods according to several metrics, while being significantly faster than its object-based counterparts. (AU)

• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático