Reducing Image Classification Errors in Convolutional Neural Networks Through a Fuzzy–Possibilistic Abstention Mechanism

A growing number of applications consume labels produced by convolutional neural networks (CNNs), yet most deployments return only the top-ranked label with no indication of how trustworthy it is; because modern networks are systematically over-confident, a high softmax probability does not by itself certify a correct decision. This paper proposes a lightweight abstention (“I do not know”) mechanism that can be added to a deployed CNN without retraining and that reuses its single forward pass. In contrast with mostly statistical treatments, uncertainty is modeled with fuzzy logic and possibility theory: three interpretable indicators (the complement of the winning probability, a Mamdani fuzzy membership for the object-to-camera acquisition distance, and a Pedrycz/Goguen fuzzy similarity between the two top-ranked classes) are aggregated with a maximum t-conorm into a possibility (uncertainty) function and its complementary confidence. The method is evaluated on twelve video sequences spanning several object categories and with two CNN backbones (VGG16 and ResNet-50). On the objects for which the backbone has a corresponding ImageNet class, a quantitative risk–coverage analysis shows that the full possibilistic rule orders predictions for abstention better than the maximum-softmax-probability and top-two-margin baselines (mean area under the risk–coverage curve, 0.148 versus 0.195 and 0.203), with the improvement coming from the geometric distance cue; on objects without a suitable ground-truth class, the rule abstains rather than emitting a wrong label. The results provide preliminary evidence that a qualitative, interpretable possibilistic rule reproduces, on real video, the abstention behavior targeted by statistical selective-classification methods, without a held-out calibration set.