  Selecting an optimal event representation is essential for event
classification in real world contexts. In this paper, we investigate the
application of qualitative spatial reasoning (QSR) frameworks for
classification of human-object interaction in three dimensional space, in
comparison with the use of quantitative feature extraction approaches for the
same purpose. In particular, we modify QSRLib, a library that allows
computation of Qualitative Spatial Relations and Calculi, and employ it for
feature extraction, before inputting features into our neural network models.
Using an experimental setup involving motion captures of human-object
interaction as three dimensional inputs, we observe that the use of qualitative
spatial features significantly improves the performance of our machine learning
algorithm against our baseline, while quantitative features of similar kinds
fail to deliver similar improvement. We also observe that sequential
representations of QSR features yield the best classification performance. A
result of our learning method is a simple approach to the qualitative
representation of 3D activities as compositions of 2D actions that can be
visualized and learned using 2-dimensional QSR.
