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33. The differences between the two ATR-42 are caused only by component n = 20. As shown in a), this component is very big; thus, it attenuates the other points. 78, the other points are less affected, so, they remain stable and recognition is made easier. 2 Octave Analysis To complete the information obtained by using the autoregressive model, the 1/12 octave analysis is used in order to get patterns and train a second neural network because although the auto-regressive model provides lineal frequency information, there is information that can go unnoticed.
This deﬁnes the Fisher criterion function F(k) , where k denotes the rank of the sorted sequence of the sorted r(k) values F(k) = μ1 (k) = [μ1 (k) − μ2 (k)]2 , σ12 (k) + σ22 (k) 1 k k = 1, . . , n − 1 , k R(i) , i=1 μ2 (k) = 1 n−k (2) n R(i) , (3) i=k+1 where the values μ1 and μ2 denote the mean values of the two classes of pixels and σ12 and σ22 stand for the variances of the aggregated distances in each of the two classes. Figure 3 shows two plots of the Fisher linear discrimination function.
Springer, Heidelberg (2006) 5. : A Fast Probabilistic Model for Hypothesis Rejection in SIFT-Based Object Recognition. , Kittler, J. ) CIARP 2006. LNCS, vol. 4225, pp. 696–705. Springer, Heidelberg (2006) 6. : Scale & Affine Invariant Interest Point Detectors. Int. Journal of Computer Vision 60(1), 63–96 (2004) 7. : A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Machine Intell. 27(10), 1615–1630 (2005) 8. : A Comparison of Affine Region Detectors. Int. Journal of Computer Vision 65(1-2), 43–72 (2005) 9.