EnTri: Ensemble Learning with Tri-level Representations for Explainable
  Scene Recognition
Amirhossein Aminimehr Amirali Molaei Erik Cambria
Scene recognition based on deep-learning has made significant progress, but
there are still limitations in its performance due to challenges posed by
inter-class similarities and intra-class dissimilarities. Furthermore, prior
research has primarily focused on improving classification accuracy, yet it has
given less attention to achieving interpretable, precise scene classification.
Therefore, we are motivated to propose EnTri, an ensemble scene recognition
framework that employs ensemble learning using a hierarchy of visual features.
EnTri represents features at three distinct levels of detail: pixel-level,
semantic segmentation-level, and object class and frequency level. By
incorporating distinct feature encoding schemes of differing complexity and
leveraging ensemble strategies, our approach aims to improve classification
accuracy while enhancing transparency and interpretability via visual and
textual explanations. To achieve interpretability, we devised an extension
algorithm that generates both visual and textual explanations highlighting
various properties of a given scene that contribute to the final prediction of
its category. This includes information about objects, statistics, spatial
layout, and textural details. Through experiments on benchmark scene
classification datasets, EnTri has demonstrated superiority in terms of
recognition accuracy, achieving competitive performance compared to
state-of-the-art approaches, with an accuracy of 87.69%, 75.56%, and 99.17% on
the MIT67, SUN397, and UIUC8 datasets, respectively.