Multimodal Images Classification using Dense SURF, Spectral Information and Support Vector Machine

The multimodal image classification is a challenging area of image processing which can be used to examine the wall painting in the cultural heritage domain. In such classification, a common space of representation is important. In this paper, we present a new method for multimodal representation learning, by using a pixel-wise feature descriptor named dense Speed Up Robust Features (SURF) combined with the spectral information carried by the pixel. For classification of extracted features we have used support vector machine (SVM). Our database was extracted from acquisition on cultural heritage wall paintings that contain four modalities UV, Visible, IRR and fluorescence. The experimental results show that the overall accuracy of this method reaches 98.1%, 92.01%, 98.2% and 94.705% in visible, fluorescence image, UVR and IRR respectively.