Fuzzy Cognitive Maps for Interpretable Image-based Classification

Image classification is a fundamental component of intelligent vision systems. Developing classifiers capable of explaining how or why a classification result occurs, in a way compatible with human perception, remains a challenge. Considering the increasing demand for such classifiers, this paper introduces a novel interpretable classification scheme based on a Fuzzy Cognitive Map (FCM), named xFCM. xFCM is a directed graph with nodes representing semantic concepts of the real world, as these are illustrated within different images. These concepts are considered as Semantic Granules (SGs) instantiated as clusters of images sharing common characteristics. The edges of the graph represent similarities between the SGs, linguistically expressed by fuzzy sets. Unlike current FCM-based classification approaches, xFCM embeds a mechanism for automatic determination of its structure from data. In addition, it is simple to implement, and it exploits cause- and-effect relationships between its concepts to derive a classification result that is interpretable by humans. The results of the experiments, using publicly available datasets, prove the effectiveness of the proposed framework, in comparison with other state-of-the-art classifiers.