Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
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X 1<br />
X i<br />
X C<br />
(a) Covariance Sum method on XNull and XRange, to obtain W Dual .<br />
or<br />
(b) Gramm-Schmidt Orthonormalization method on [W Null<br />
orthonormal basis W Dual .<br />
7. Select optimal feature set by using either<br />
opt ,W Range<br />
opt<br />
(a) Forward Selection procedure to select optimal features W Dual<br />
opt<br />
based on class separability criterion given in Eqn. 4.28.<br />
or<br />
(b) Backward Selection in the same manner to form W Dual<br />
opt .<br />
8. Project all training samples on W Dual<br />
opt .<br />
9. Recognition stage:<br />
(a) Project probe sample, presented for recognition, on W Dual<br />
opt .<br />
], to obtain<br />
from W Dual<br />
(b) Find distance <strong>of</strong> the projected probe to nearest training sample from each<br />
class.<br />
(c) Label the probe with the class corresponding to the minimum distance<br />
value.<br />
µ1<br />
µi<br />
µC<br />
Null Space<br />
<strong>of</strong> Sw<br />
Range Space<br />
<strong>of</strong> Sw<br />
XNull<br />
x 1 Null<br />
x i Null<br />
x C Null<br />
XRange<br />
x 1 Range<br />
x i Range<br />
x C Range<br />
W Null<br />
opt<br />
W Range<br />
opt<br />
Fusion W Dual<br />
Figure 4.3: Architecture <strong>of</strong> our proposed method <strong>of</strong> feature fusion.<br />
82<br />
Backward/<br />
Forward<br />
Selection<br />
W Dual<br />
opt