  The complexity of state-of-the-art modeling techniques for image
classification impedes the ability to explain model predictions in an
interpretable way. Existing explanation methods generally create importance
rankings in terms of pixels or pixel groups. However, the resulting
explanations lack an optimal size, do not consider feature dependence and are
only related to one class. Counterfactual explanation methods are considered
promising to explain complex model decisions, since they are associated with a
high degree of human interpretability. In this paper, SEDC is introduced as a
model-agnostic instance-level explanation method for image classification to
obtain visual counterfactual explanations. For a given image, SEDC searches a
small set of segments that, in case of removal, alters the classification. As
image classification tasks are typically multiclass problems, SEDC-T is
proposed as an alternative method that allows specifying a target
counterfactual class. We compare SEDC(-T) with popular feature importance
methods such as LRP, LIME and SHAP, and we describe how the mentioned
importance ranking issues are addressed. Moreover, concrete examples and
experiments illustrate the potential of our approach (1) to obtain trust and
insight, and (2) to obtain input for model improvement by explaining
misclassifications.
