Optimal Filter Selection for Multispectral Object Classification Using Fast Binary Search

TL;DR

This paper presents a fast binary search algorithm for selecting optimal filters in multispectral imaging, achieving the same results as full search but with significantly reduced computational complexity, improving classification accuracy.

Contribution

Introduction of a novel fast binary search method for optimal filter selection that guarantees minimum spectral distance and outperforms existing techniques.

Findings

Matches full search optimal solutions at lower complexity

Maintains constant complexity regardless of filter number

Reduces misclassification in real-world tests

Abstract

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.