Combining Deep Learning and Multi-class Discriminant Analysis for Granite Tiles Classification

Convolutional neural networks (CNNs) have become the state-of-the-art in automatic feature learning which has led to outstanding performance on pattern recognition applications. On the other hand, methodologies for discriminant analysis on multiclass problems have been proposed to determine the discriminant contribution of each feature. In this paper, we combine the capabilities of CNN and multi-class discriminant analysis to propose a general data-driven methodology for feature learning and discriminability for texture classification. The whole pipeline has two main blocks: (a) An approach that understands intrinsic patterns in small image patches using CNNs customized for the focused problem; (b) A multi-class discriminant analysis technique, fed with the CNN output, to select the most discriminant features for classification tasks. Our experimental results have shown that the feature spaces generated by the combination of CNN and discriminant analysis allow higher recognition rates using very much less CNN features in five-class granite image analysis.