%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2021/09.14.21.04
%2 sid.inpe.br/sibgrapi/2021/09.14.21.04.07
%@doi 10.1109/SIBGRAPI54419.2021.00054
%T Towards a Simple and Efficient Object-based Superpixel Delineation Framework
%D 2021
%A Belém, Felipe de Castro,
%A Perret, Benjamin,
%A Cousty, Jean,
%A Guimarães, Silvio Jamil Ferzoli,
%A Falcão, Alexandre Xavier,
%@affiliation University of Campinas
%@affiliation Université Gustave Eiffel
%@affiliation Université Gustave Eiffel
%@affiliation Pontifical Catholic University of Minas Gerais
%@affiliation University of Campinas
%E Paiva, Afonso ,
%E Menotti, David ,
%E Baranoski, Gladimir V. G. ,
%E Proença, Hugo Pedro ,
%E Junior, Antonio Lopes Apolinario ,
%E Papa, João Paulo ,
%E Pagliosa, Paulo ,
%E dos Santos, Thiago Oliveira ,
%E e Sá, Asla Medeiros ,
%E da Silveira, Thiago Lopes Trugillo ,
%E Brazil, Emilio Vital ,
%E Ponti, Moacir A. ,
%E Fernandes, Leandro A. F. ,
%E Avila, Sandra,
%B Conference on Graphics, Patterns and Images, 34 (SIBGRAPI)
%C Gramado, RS, Brazil (virtual)
%8 18-22 Oct. 2021
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K object-based,Image Foresting Transform,Superpixels,Saliency,Segmentation.
%X 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.
%@language en
%3 2021_SIBGRAPI_ODISF.pdf