Class-Specific Material Categorisation

Although a considerable amount of work has been published on material classification, relatively little of it studies situations with considerable variation within each class. Many experiments use the exact same sample, or different patches from the same image, for training and test sets. Thus, such studies are vulnerable to effectively recognising one particular sample of a material as opposed to the material category. In contrast, this paper places firm emphasis on the capability to generalise to previously unseen instances of materials. We adopt an appearance-based strategy, and conduct experiments on a new database which contains several samples of each of eleven material categories, imaged under a variety of pose, illumination and scale conditions. Together, these sources of intra-class variation provide a stern challenge indeed for recognition. Somewhat surprisingly, the difference in performance between various state-of-the-art texture descriptors proves rather small in this ta...

• Type: Conference
• Year: 2005
• Where: ICCV
• Authors: Barbara Caputo, Eric Hayman, P. Mallikarjuna
• Keywords: Appropriate Kernel Parameters, Computer Vision, Considerable Variation, ICCV 2005, Kernel Parameter Optimal, Material Classification, State-of-the-art Texture Descriptors