On the Effectiveness of Heterogeneous Ensemble Methods for Re-identification
This provides a more efficient solution for re-identification in hardware-restricted industrial scenarios, though it is incremental as it builds on existing ensemble and feature extraction techniques.
The paper tackles the problem of re-identifying industrial entities by introducing a heterogeneous ensemble method that replaces complex siamese neural networks with simplified models, achieving state-of-the-art performance with a Rank-1 accuracy of over 77% and Rank-10 accuracy of over 99%.
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.