Authors: J.J. Charles · L.I. Kuncheva · B. Wells · I.S. Lim

DOI: 10.1007/s11004-009-9219-3

Abstract:

This paper investigates the stability of an automatic system for classifying
kerogen material from images of sieved rock samples. The system comprises four
stages: image acquisition, background removal, segmentation, and classification of
the segmented kerogen pieces as either inertinite or vitrinite. Depending upon a segmentation
parameter d, called “overlap”, touching pieces of kerogen may be split
differently. The aim of this study is to establish how robust the classification result is
to variations of the segmentation parameter. There are two issues that pose difficulties
in carrying out an experiment. First, even a trained professional may be uncertain
when distinguishing between isolated pieces of inertinite and vitrinite, extracted
from transmitted-light microscope images. Second, because manual labelling of large
amount of data for training the system is an arduous task, we acquired the true labels
(ground truth) only for the pieces obtained at overlap d = 0.5. To construct ground
truth for various values of d we propose here label-inheritance trees. With thus estimated
ground truth, an experiment was carried out to evaluate the robustness of the
system to changes in the segmentation through varying the overlap value d. The average
system accuracy across values of d spanning the range from 0 to 1 was 86.5%,
which is only slightly lower than the accuracy of the system at the design value of
d = 0.5 (89.07%).

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