PHD Thesis - Institute for Computer Graphics and Vision - Graz ...

4.6. Experimental evaluation 58
MSER detector accomplishes in average the best matching scores. The DOG detector shows
surprisingly low matching scores. While it starts similar to the other detectors the matching
score drops very fast with increasing viewpoint change. This is once more surprising as the DOG
detector was initially introduced with the SIFT descriptor. Most impressive are the results for
the simple point detectors. For small viewpoint changes the results are ranked under the top
three. With increasing viewpoint change the matching scores however drop dramatically.
Comparing these results to the previous evaluation of Mikolajczyk [74] on planar scenes
only, one can see two main differences. First, the MSER detector provides a significant higher
performance than other affine invariant detectors especially for the matching score. This comes
from the fact that the MSER detector is not corner based and does not tend to detect regions
on depth continuities. Second, the evaluations on the ”Room” scene show that the achievable
repeatability scores and matching scores for complex scenes are considerably lower than those
achieved on planar scenes. This means one must expect a much lower number of matches in
practice than the previous evaluations suggested.
4.6.2 Combining local detectors
This experiment evaluates the benefits gained by combining different detectors. Benefits can be
gained if the combined detectors produce detections in different parts of the image. To assess
this we measure the complementary score c n i . Figure 4.10 shows a cumulative plot of the relative
numbers of non-overlapping matched interest regions from 5 different detectors for the ”Group”
and ”Room” scene. Every line shows how many new regions are added to the previous set of
interest regions by the specific detector. The graphs show impressively that combining local
detectors leads to a larger set of distinct image regions over a wide range of viewpoint changes.
It is remarkable that the regions from a combination of all 5 detectors still contain less than 20%
overlapping ones. However, in real applications usually performance issues do not permit to run
all detectors on an input image. A good choice for combining 2 detectors would be selecting
the MSER and DOG detector which apparently seem to be the 2 fastest detectors. The graphs
in Figure 4.11 show only a small number of overlapping regions. Combining Harris-Affine and
Hessian-Affine detectors together creates a significant number of overlapping regions as seen in
Figure 4.12. This is expected as the algorithms for both methods are quite similar.