Modified Jensen-Bregman LogDet Divergence for Target Detection With Region Covariance Descriptor

In this study, we exploit the modified Jensen-Bregman LogDet (MJBLD) divergence to measure the dissimilarity between two region covariance descriptors extracted from an image, and design a target detection method based on this descriptor. In particular, MJBLD divergence, which considers the non-Euclidean geometric structure, is used as the measurement on the symmetric positive-definite (SPD) matrix manifold. The MJBLD divergence is a modified version of the Jensen-Bregman LogDet (JBLD) divergence which has many properties similar to the affine invariant Riemannian metric. Then, the MJBLD divergence is applied for the task of the image target detection where the image region of interest is represented as a covariance descriptor. The covariance descriptor is a SPD matrix which is constructed by the first and second gradients of intensity and the three-dimensional color information. Since the SPD matrix naturally resides on the non-Euclidean Riemannian manifold and the MJBLD divergence can be treated as a manifold metric, applying the non-Euclidean distance to SPD matrices can yield a better performance in comparison with the Euclidean distance. Experimental results show that our proposed method outperforms the state-of-the-art method.