An ensemble of classifiers based on different texture descriptors for texture classification

Here we propose a system that incorporates two different state-of-the-art classifiers (support vector machine and gaussian process classifier) and two different descriptors (multi local quinary patterns and multi local phase quantization with ternary coding) for texture classification. both the tested descriptors are an ensemble of stand-alone descriptors obtained using different parameters setting (the same set is used in each dataset). for each stand-alone descriptor we train a different classifier, the set of scores of each classifier is normalized to mean equal to zero and standard deviation equal to one, then all the score sets are combined by the sum rule. our experimental section shows that we succeed in building a high performance ensemble that works well on different datasets without any ad hoc parameters tuning. the fusion among the different systems permits to outperform svm where the parameters and kernels are tuned separately in each dataset, while in the proposed ensemble the linear svm, with the same parameter cost in all the datasets, is used.