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CRITERIA FOR SELECTION AND EXTRACTION                        193

            respectively. Then, it can be shown that the Chernoff distance
            transforms into:

                       1                T                1
                J ðsÞ¼ sð1   sÞðm   m Þ ½ð1   sÞC 1 þ sC 2 Š ðm   m Þ
                                 2
                 C
                                      1
                                                            2
                                                                 1
                       2
                            "                #                         ð6:19Þ
                         1   jð1   sÞC 1 þ sC 2 j
                       þ ln
                         2      jC 1 j 1 s jC 2 j s
            It can be seen that if the covariance matrices are independent of the
            classes, e.g. C 1 ¼ C 2 , the second term vanishes, and the Chernoff and the
            Bhattacharyya distances become proportional to the Mahalanobis distance
            SNR given in (2.46): J BHAT  ¼ SNR/8. Figure 6.3(a) shows the corresponding
            Chernoff and Bhattacharyya upper bounds. In this particular case, the
            relation between SNR and the minimum error rate is easily obtained using
            expression (2.47). Figure 6.3(a) also shows the Bhattacharyya lower bound.
              The dependency of the Chernoff bound on s is depicted in Figure 6.3(b).
            If C 1 ¼ C 2 , the Chernoff distance is symmetric in s, and the minimum
            bound is located at s ¼ 0:5 (i.e. the Bhattacharyya upper bound). If the
            covariance matrices are not equal, the Chernoff distance is not symmetric,
            and the minimum bound is not guaranteed to be located midway. A
            numerical optimization procedure can be applied to find the tightest bound.
              If in the Gaussian case, the expectation vectors are equal (m ¼ m ),
                                                                     1
                                                                          2
            the first term of (6.19) vanishes, and all class information is represented
            by the second term. This term corresponds to class information carried
            by differences in covariance matrices.


             (a)                                      (b)

              0.5  E                                   0.5 E
                      Bhattacharyya lower bound
                        Chernoff bound (s = 0.5)
                          = Bhattacharyya upper bound        SNR =10
             0.25                                     0.25
                               Chernoff bound (s = 0.8)
                     E min
                0                                        0
                 0      10     20      30  SNR 40        0       0.5    S  1

            Figure 6.3 Error bounds and the true minimum error for the Gaussian case
            (C 1 ¼ C 2 ). (a) The minimum error rate with some bounds given by the Chernoff
            distance. In this example the bound with s ¼ 0:5 (Bhattacharyya upper bound) is the
            most tight. The figure also shows the Bhattacharyya lower bound. (b) The Chernoff
            bound with dependence on s
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