  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.
