  Scene recognition model based on the DNN and game theory with its
applications in human-robot interaction is proposed in this paper. The use of
deep learning methods in the field of scene recognition is still in its
infancy, but has become an important trend in the future. As the innovative
idea of the paper, we propose the following novelties. (1) In this paper, the
image registration problem is transformed into a problem of minimum energy in
Markov Random Field to finalize the image pre-processing task. Game theory is
used to find the optimal. (2) We select neighboring homogeneous sample features
and the neighboring heterogeneous sample features for the extracted sample
features to build a triple and modify the traditional neural network to propose
the novel DNN for scene understanding. (3) The robot control is well combined
to guide the robot vision for multiple tasks. The experiment is then conducted
to validate the overall performance.
