Authors: Omar Herouane; Lahcen Moumoun; Taoufiq Gadi; Mohamed Chahhou
Addresses: LIIMCS Laboratory, Faculty of Science and Technology, Hassan 1st University, Settat, Morocco ' LIIMCS Laboratory, Faculty of Science and Technology, Hassan 1st University, Settat, Morocco ' LIIMCS Laboratory, Faculty of Science and Technology, Hassan 1st University, Settat, Morocco ' Faculty of Science Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fes, Morocco
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.
Keywords: boosting algorithm; Adaboost; machine learning; Minkowski metric; segmentation; labelling; evaluation; shape index; shape spectrum descriptor; 3D object; computer vision.
International Journal of Computational Vision and Robotics, 2018 Vol.8 No.5, pp.509 - 525
Received: 03 May 2017
Accepted: 09 Jan 2018
Published online: 24 Sep 2018 *