Interest of the combination of classifiers for volumetric textures classification

Nowadays, classification is applied in various fields such as pattern and writing recognition, prints checking, faces identification, medical images analysis, 2D textures characterization and volumetric textures characterization. Indeed, the three-dimensional field is considered among one of the most important fields in image processing because of the great quantity of information that can be extracted. In this work, we try to improve the performances of classification for volumetric textures images by proposing a multiple classifier systems (MCS) based method combining three Euclidean classifiers: simple Euclidean classifier (ES), normal Euclidean classifier (EN) and balanced Euclidean classifier (EB). Thereafter, we compared the performance of the proposed method to the Euclidean methods (ES, EN and EB). The hybrid presented approach has proven to be more efficient in classification and mostly robust against Gaussian noise.