  In this contribution, we introduce a novel ensemble method for the
re-identification of industrial entities, using images of chipwood pallets and
galvanized metal plates as dataset examples. Our algorithms replace commonly
used, complex siamese neural networks with an ensemble of simplified,
rudimentary models, providing wider applicability, especially in
hardware-restricted scenarios. Each ensemble sub-model uses different types of
extracted features of the given data as its input, allowing for the creation of
effective ensembles in a fraction of the training duration needed for more
complex state-of-the-art models. We reach state-of-the-art performance at our
task, with a Rank-1 accuracy of over 77% and a Rank-10 accuracy of over 99%,
and introduce five distinct feature extraction approaches, and study their
combination using different ensemble methods.
