  Scene Classification has been addressed with numerous techniques in computer
vision literature. However, with the increasing number of scene classes in
datasets in the field, it has become difficult to achieve high accuracy in the
context of robotics. In this paper, we implement an approach which combines
traditional deep learning techniques with natural language processing methods
to generate a word embedding based Scene Classification algorithm. We use the
key idea that context (objects in the scene) of an image should be
representative of the scene label meaning a group of objects could assist to
predict the scene class. Objects present in the scene are represented by
vectors and the images are re-classified based on the objects present in the
scene to refine the initial classification by a Convolutional Neural Network
(CNN). In our approach we address indoor Scene Classification task using a
model trained with a reduced pre-processed version of the Places365 dataset and
an empirical analysis is done on a real-world dataset that we built by
capturing image sequences using a GoPro camera. We also report results obtained
on a subset of the Places365 dataset using our approach and additionally show a
deployment of our approach on a robot operating in a real-world environment.
