Labelling and evaluation of 3D objects segmentation using multiclass boosting algorithm Online publication date: Fri, 28-Sep-2018
by Omar Herouane; Lahcen Moumoun; Taoufiq Gadi; Mohamed Chahhou
International Journal of Computational Vision and Robotics (IJCVR), Vol. 8, No. 5, 2018
Abstract: 3D objects segmentation has become a fundamental task in computer vision and digital multimedia. Evaluating the automatic 3D segmentation algorithm's quality and comparison of their performances are important topics, some metrics are proposed in the literature. However, these metrics are not too prominent for evaluating the automatic segmentation algorithms in general. The aim of this paper is to introduce a simple and efficient approach to evaluate the automatic segmentation of a 3D object. This new evaluation scheme is based on learning 3D mesh and on the Minkowski metric. Machine learning is used to learn a function that assigns the appropriate label to each part of a segmented 3D object of the database; then, the error committed by each labelled segment is computed using the Minkowski norm. The best performance and high quality of the quantitative results demonstrated the efficiency of the proposed approach.
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