论文标题
使用快速二进制搜索的多光谱对象分类的最佳过滤器选择
Optimal Filter Selection for Multispectral Object Classification Using Fast Binary Search
论文作者
论文摘要
在设计用于分类不同光谱的多光谱成像系统时，有必要从具有数百个不同不同的集合中选择少量的过滤器。通过完整的搜索解决此问题，会导致大量检查可能性，并且是NP-HARD。在本文中，我们介绍了一种新颖的快速二进制搜索以进行最佳滤波器选择，以确保不同光谱之间的最小距离度量进行分类。在我们的实验中，此过程达到了与完整搜索相同的最佳解决方案。所需的过滤器数量会影响阶乘顺序中的完整搜索，而快速二进制搜索保持恒定。因此，快速的二进制搜索可以在足够的时间内找到所有组合的最佳解决方案，并避免启发术。此外，我们的快速二进制搜索算法在现实世界中的分类问题中，在错误分类的光谱方面优于其他过滤器选择技术。
When designing multispectral imaging systems for classifying different spectra it is necessary to choose a small number of filters from a set with several hundred different ones. Tackling this problem by full search leads to a tremendous number of possibilities to check and is NP-hard. In this paper we introduce a novel fast binary search for optimal filter selection that guarantees a minimum distance metric between the different spectra to classify. In our experiments, this procedure reaches the same optimal solution as with full search at much lower complexity. The desired number of filters influences the full search in factorial order while the fast binary search stays constant. Thus, fast binary search allows to find the optimal solution of all combinations in an adequate amount of time and avoids prevailing heuristics. Moreover, our fast binary search algorithm outperforms other filter selection techniques in terms of misclassified spectra in a real-world classification problem.
