  Convolutional Neural Networks (CNNs) are artificial learning systems
typically based on two operations: convolution, which implements feature
extraction through filtering, and pooling, which implements dimensionality
reduction. The impact of pooling in the classification performance of the CNNs
has been highlighted in several previous works, and a variety of alternative
pooling operators have been proposed. However, only a few of them tackle with
the uncertainty that is naturally propagated from the input layer to the
feature maps of the hidden layers through convolutions. In this paper we
present a novel pooling operation based on (type-1) fuzzy sets to cope with the
local imprecision of the feature maps, and we investigate its performance in
the context of image classification. Fuzzy pooling is performed by
fuzzification, aggregation and defuzzification of feature map neighborhoods. It
is used for the construction of a fuzzy pooling layer that can be applied as a
drop-in replacement of the current, crisp, pooling layers of CNN architectures.
Several experiments using publicly available datasets show that the proposed
approach can enhance the classification performance of a CNN. A comparative
evaluation shows that it outperforms state-of-the-art pooling approaches.
