Improving Ferns Ensembles by Sparsifying and Quantising Posterior Probabilities

Antonio L. Rodriguez, Vitor Sequeira; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 4103-4111

Ferns ensembles offer an accurate and efficient multiclass non-linear classification, commonly at the expense of consuming a large amount of memory. We introduce a two-fold contribution that produces large reductions in their memory consumption. First, an efficient L0 regularised cost optimisation finds a sparse representation of the posterior probabilities in the ensemble by discarding elements with zero contribution to valid responses in the training samples. As a by-product this can produce a prediction accuracy gain that, if required, can be traded for further reductions in memory size and prediction time. Secondly, posterior probabilities are quantised and stored in a memory-friendly sparse data structure. We reported a minimum of 75% memory reduction for different types of classification problems using generative and discriminative ferns ensembles, without increasing prediction time or classification error. For image patch recognition our proposal produced a 90% memory reduction, and improved in several percentage points the prediction accuracy.