Simplifying Gaussian mixture model via model similarity

Mixture models are crucial statistical modeling tools at the heart of many challenging applications in computer vision, pattern recognition, and etc. Simplification of mixture models has recently emerged as an important issue in the field of statistical learning. In this paper, we propose a novel Gaussian mixture model simplification approach using only the models parameters, avoiding the use of the original data records which may bring heavy computational and storage burden. We integrate the inter-model similarity and intra-model independence to introduce a similarity measure between two Gaussian mixture models. An objective function is further designed which aims at keeping a balance between model similarity and simplification degree and a heuristic simulated annealing method is presented to search for the optimal parameter set of the simplified model. The experimental results confirm that our approach is effective and promising.