K.N. Toosi University of Technology International Journal of Robotics, Theory and Applications 2008-7144 4 2 2015 09 01 3D Scene and Object Classification Based on Information Complexity of Depth Data 28 35 12523 EN Hamid D. Taghirad Industrial Control Center of Excellence (ICCE), Advanced Robotics and Automated Systems (ARAS), Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran, P. O. Box 16315-1355 0000-0002-0615-6730 Alireza Norouzzadeh Industrial Control Center of Excellence (ICCE), Advanced Robotics and Automated Systems (ARAS), Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran, P. O. Box 16315-1355 Journal Article 2016 01 16 In this paper the problem of 3D scene and object classification from depth data is addressed. In contrast to high-dimensional feature-based representation, the depth data is described in a low dimensional space. In order to remedy the curse of dimensionality problem, the depth data is described by a sparse model over a learned dictionary. Exploiting the algorithmic information theory, a new definition for the Kolmogorov complexity is presented based on the Earth Mover’s Distance (EMD). Finally the classification of 3D scenes and objects is accomplished by means of a normalized complexity distance, where its applicability in practice is proved by some experiments on publicly available datasets. Also, the experimental results are compared to some state-of-the-art 3D object classification methods. Furthermore, it has been shown that the proposed method outperforms FAB-Map 2.0 in detecting loop closures, in the sense of the precision and recall. https://ijr.kntu.ac.ir/article_12523_3f62d610adac92bbf2996bb4d8ff7657.pdf