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.

可以使用投影签名来完成低级对象匹配。在大量投影的情况下，匹配算法必须处理不太重要的切片。一种简单的方法是进行统计分析或应用机器学习来确定重要特征。为了考虑投影矩阵的相邻值，应使用卷积神经网络。为了比较两个矩阵，可以使用卷积头的连体结构。本文设计并实现了一个基于多方向图像投影数据的暹罗卷积神经网络对象匹配性能分析实验。使用基于回溯搜索的神经结构生成方法来创建卷积结构，并使用基于最长处理时间启发式算法的具有高效调度的主/工人结构化分布式处理来并行训练和评估模型。结果表明，基于投影的方法在单次分类精度和内存消耗方面是帕累托最优的。