Support vector machines for improved multiaspect target recognition using the Fisher kernel scores of Hidden Markov Models
In conjunction with physics-based feature extraction, Hidden Markov Model (HMM) classifiers have been used successfully to fuse scattering data from multiple target orientations where the target-sensor orientation is generally unknown or "hidden" . The use of prior knowledge concerning sensor motion is employed in modeling the sequential data, improving classification performance. However, the assumptions of first order Markovian state transitions state-dependent statistics constrain the intrinsic class of pdf structures admitted by the HMM, for use in classification. In this paper we overcome the above limitation by using the local variations in the HMMs induced by each sequence of observations as the feature vector for a support vector machine (SVM) classifier. Improved discrimination results are presented for measured acoustic scattering data.