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Mining 3D-Structures: Subparts Extraction and Transfer Learning

The massive use of data mining in industrial contexts faces the problem of the results understanding by users. For example, until now, geo-scientists showdown their adoptions of these systems because of the intuitive part of their work, which requires to well understand a geological study instead of only trusting a computational system. In this paper, a new supervised machine learning based approach to build classifier from 3D-objects is presented. In this approach, first of all, an extraction of pertinent subparts of the objects is performed to highlight subparts characterising the most each kind of 3D-objects. Afterwards, a classical machine learning model is applied to build a classifier based on these pertinent subparts. The main idea is to use discriminant subparts of 3D-objects for the supervised classification in order to take care of the local nature of pertinent elements. This allows the user to be aware of these subparts which have been useful to determine the corresponding class of the object. Two algorithms are presented the 3DRESC algorithm, and its extension, the 3DRESC-TF algorithm, based on the used of transfer learning to improve its performance. Final results are presented that highlight the advantages of this new approach to build description features of 3D-objects and of the introduction of transfer learning.