  Environmental microorganism (EM) offers a high-efficient, harmless, and
low-cost solution to environmental pollution. They are used in sanitation,
monitoring, and decomposition of environmental pollutants. However, this
depends on the proper identification of suitable microorganisms. In order to
fasten, low the cost, increase consistency and accuracy of identification, we
propose the novel pairwise deep learning features to analyze microorganisms.
The pairwise deep learning features technique combines the capability of
handcrafted and deep learning features. In this technique we, leverage the Shi
and Tomasi interest points by extracting deep learning features from patches
which are centered at interest points locations. Then, to increase the number
of potential features that have intermediate spatial characteristics between
nearby interest points, we use Delaunay triangulation theorem and straight-line
geometric theorem to pair the nearby deep learning features. The potential of
pairwise features is justified on the classification of EMs using SVMs, k-NN,
and Random Forest classifier. The pairwise features obtain outstanding results
of 99.17%, 91.34%, 91.32%, 91.48%, and 99.56%, which are the increase of about
5.95%, 62.40%, 62.37%, 61.84%, and 3.23% in accuracy, F1-score, recall,
precision, and specificity respectively, compared to non-paired deep learning
features.
