ISPRS Commission III, Vol.34, Part 3A ,,Photogrammetric Computer Vision“, Graz, 2002
and C, and we determine the canonical feature 7; which
maximizes Vo; (and thus represents the baseline of con-
sensus) and the redundancy Redp,. We keep the subset of
features which maximizes Redp,, among all the possible
combinations (the process takes less than 15 minutes on a
333 MHz processor). We show for each group on table 1
r Features Redp,
Gl 5 Ia, C , C4, 151, dr» 0.59
G2 7 Ia, D, R, dus Ch Ms1, d,2 0.37
G3 10 Ia, D, R, duis dys, Cha, de, Ms1, 0.68
dy», dra
GA 8 ] Ia. C, D. 5,6, d. ds, dr: 0.64
Table 1: Feature subsets which maximize redundancy.
the feature subset that maximizes the redundancy and the
value of the redundancy criterion. Groups 3 and 4 have
the highest scores, so one can claim that the subjective
and objective features are substantially related along the
first canonical dimension. But this extent of agreement be-
tween numerical features and evaluators cannot be reached
for group 2, which shows quite a low redundancy score.
When we compare the behavior of the mean of the marks
(recall that the PCA has revealed a 1-dimensional evalua-
tor space) and that of the canonical features, we observe
that some segmentations cause great discrepancy. In sim-
pler words for these images the note of the evaluators can-
not be predicted based on the chosen feature set. Thus
we developed a procedure to eliminate segmentations that
cause conflicting votes among the evaluators and kept only
the consensus images, that is those images that received
the same relative ranking from all the evaluators. CA re-
sults with the pruned consensus set are shown in table 2.
This second table reveals a great improvement both in the
r Features Redp,
Gl 7 Ia, C,D,R. ds Caet d. 0.73
G2 6 | fa, durs Ca.£t Mar. d,2 et di» 0.40
G3 10 Ia, C > D. R, dui: du2, Ca, mga, 0.79
dy» et dr»
G4 7 Ta 4,4102: 4. dec, dy2 et dr» 0.80
Table 2: Feature subsets which maximize redundancy
(learning on images of consensus).
consistency between the selected features across groups as
well as in the redundancy marks. While groups 1, 3 and 4
show this improvement, group 2 remains still a poor pre-
dictor of evaluator marks from the features. Hence we re-
moved this group from the rest of the experiment.
Thus we get three psychovisual feature subsets which pre-
dict the vote ofthe evaluators reasonably well. One method
to collapse these three sets to one “best” set would be to
use cross-validation across groups. For example we use
the feature set of group 1 and use it on predicting the data
of the other two groups, i.e., groups 3 and 4, and calculate
the redundancies Red(Ia, C, D, R, duo, C4, de) on these
groups. We repeat this calculation similarly for the other
two feature sets. Then we take the average of the redundan-
cies of each feature set on the three segmentation groups,
and choose the largest one.
6 CONCLUSION
We have presented the framework for a new feature ex-
traction method for a task-oriented segmentation that com-
bines both the statistical properties of image features and
segmentation quality assessments of a jury. The method-
ology has been applied to the task of segmentation build-
ings in medium-resolution images. This study was the first
step for the extraction of psychovisual image features. The
work will continue to build membership functions of fea-
tures in a Tverskian context.
ACKNOWLEDGMENTS
We would like to thank Pr. B. Burtschy (from ENST) for
his valuable advice during discussions on canonical analy-
sis.
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