2D motif basis applied to the classification of digital images

The classification of raw data often involves the problem of selecting the appropriate set of features to represent the input data. Different types of features can be extracted from the input dataset, but only some of them are actually relevant for the classification process. Since relevant features are often unknown in real-world problems, many candidate features are usually introduced. This degrades both the speed and the predictive accuracy of the classifier due to the presence of redundancy in the set of candidate features. Recently, a special class of bidimensional motifs, i.e. 2D motif basis has been introduced in the literature. 2D motif basis showed to be powerful in capturing the relevant information of digital images, also achieving good performances for image compression. Here, we investigate the effectiveness of 2D motif basis, when they are used as features for image classification. We embed such features in a bag-of-words model, and then we apply KNearest Neighbour for the classification step. Results obtained on both benchmark image datasets and video frames datasets show that, despite the pixel-level nature of the considered features, the achieved accuracy is high and comparable with that of other techniques proposed in the literature.