Abstract

Classifying materials from their appearance is a challenging problem, especially if illumination and pose conditions are permitted to change: highlights and shadows caused by 3D structure can radically alter a sample’s visual texture. Despitethesedifficulties,researchershavedemonstratedimpressiveresultsonthe CUReT database which contains many images of 61 materials under different conditions.A first contribution of this paper is to further advance the state-of-theart by applying SupportVector Machines to this problem. To our knowledge, we record the best results to date on the CUReT database. Inourworkweadditionallyinvestigatetheeffectofscalesincerobustnesstoviewing distance and zoom settings is crucial in many real-world situations. Indeed, a material’s appearance can vary considerably as fine-level detail becomes visible or disappears as the camera moves towards or away from the subject. We handle scale-variations using a pure-learning approach, incorporating samples imaged at differentdistancesintothetrainingset.Anempiricalinvestigationisconductedto show how the classification accuracy decreases as less scale information is made available during training.