Comparative analysis of image projection-based descriptors in Siamese neural networks

• Published in: Advances in engineering software (1992) Vol. 154; p. 102963
• Main Authors: Kertész, Gábor, Szénási, Sándor, Vámossy, Zoltán
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2021
• Subjects: Multi-directional image projections; Neural architecture generation; one-shot classification accuracy; Siamese neural networks; Vehicle re-identification; Multi-directional image projections; Neural architecture generation; Siamese neural networks; Vehicle re-identification; one-shot classification accuracy
• ISSN: 0965-9978
• DOI: 10.1016/j.advengsoft.2020.102963

•Siamese neural networks were trained on image projection matrices for a vehicle re-identification task.•A neural architecture generation method based on backtracking search is used for convolutional architecture generation.•The distributed training is done in a master/worker structure with LPT-heuristics based scheduling on estimated complexity.•Projection-based methods are Pareto optimal in terms of one-shot classification accuracy and memory consumption. Low-level object matching can be done using projection signatures. In case of a large number of projections, the matching algorithm has to deal with less significant slices. A trivial approach would be to do statistical analysis or apply machine learning to determine the significant features. To take adjacent values of the projection matrices into account, a convolutional neural network should be used. To compare two matrices, a Siamese-structure of convolutional heads can be applied. In this paper, an experiment is designed and implemented to analyze the object matching performance of Siamese Convolutional Neural Networks based on multi-directional image projection data. A backtracking search-based Neural Architecture Generation method is used to create convolutional architectures, and a Master/Worker structured distributed processing with highly efficient scheduling based on the Longest Processing Times-heuristics is used for parallel training and evaluation of the models. Results show that the projection-based methods are Pareto optimal in terms of one-shot classification accuracy and memory consumption.