Improving Image Classification for Geospatial Data using a Semantic Referee

Tracking #: 1862-3075

This paper is currently under review
Authors: 
Marjan Alirezaie
Martin Längkvist
Michael Sioutis
Amy Loutfi

Responsible editor: 
Guest Editors Semantic Deep Learning 2018

Submission type: 
Full Paper
Abstract: 
Recent machine learning algorithms have shown a considerable success in various computer vision tasks, including semantic segmentation. However, they seldom perform without error. A key aspect of discovering why the algorithm has failed is usually the task of the human who, using domain knowledge and contextual information, can discover systematic shortcomings in either the data or the algorithm. In this paper, we propose a symbolic-based technique, called a semantic referee, which is both able to explain the errors emerging from the machine learning framework and suggest corrections. The semantic referee relies on a spatial reasoning method applied on ontological knowledge in order to retrieve the features of the errors in terms of their spatial relations with their environment. The symbolic explanation of the errors is then reported to the learning algorithm to learn from its mistakes and consequently improve the performance. In this paper, the proposed method of the interaction between a neural network classifier and a semantic referee show how to improve the performance of semantic segmentation for satellite imagery data.
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