Markov chain models (MCMs) were recently adopted in many recognition applications. The well-known clustering algorithm, the k-means algorithm, is used to design the codebooks of the MCM, and then each code word in the codebook is regarded as one state of MCM. However, users usually have no idea how to determine the number of states before the design of the MCM, and therefore doubt whether the MCM produced by the k-means algorithm is optimal. We propose a new MCM based on the genetic algorithm for recognition applications. Genetic algorithms combine the clustering algorithm and the MCM design. The users do not need to define the size of the codebook before the design of the MCM. The genetic algorithm can automatically find the number of states in MCM, and thereby obtain a near-optimal MCM. Furthermore, we propose the fuzzy MCM (FMCM) and the fuzzy genetic algorithm (FGA) to enhance the recognition rate. Experimental results show that the proposed MCM outperforms the traditional MCM and other texture and speech recognition methods.
"Markov chain models based on genetic algorithms for texture and speech recognition," Journal of Electronic Imaging 15(3), 033004 (1 July 2006). https://doi.org/10.1117/1.2234731