On hidden Markov models and cyclic strings for shape recognition
Starting point invariance in contour classification is not achieved with current HMM proposals. Cyclic strings are adequate for achieving starting point invariance. We modify the BaumWelch and Viterbi algorithms for dealing with cyclic strings. A new model, cyclic linear HMMs, speeds up training and classification of cyclic strings. Our experiments show that our proposals outperform other methods in the literature.
hidden markov models cyclic strings shape recognition