  Leather is a type of natural, durable, flexible, soft, supple and pliable
material with smooth texture. It is commonly used as a raw material to
manufacture luxury consumer goods for high-end customers. To ensure good
quality control on the leather products, one of the critical processes is the
visual inspection step to spot the random defects on the leather surfaces and
it is usually conducted by experienced experts. This paper presents an
automatic mechanism to perform the leather defect classification. In
particular, we focus on detecting tick-bite defects on a specific type of calf
leather. Both the handcrafted feature extractors (i.e., edge detectors and
statistical approach) and data-driven (i.e., artificial neural network) methods
are utilized to represent the leather patches. Then, multiple classifiers
(i.e., decision trees, Support Vector Machines, nearest neighbour and ensemble
classifiers) are exploited to determine whether the test sample patches contain
defective segments. Using the proposed method, we managed to get a
classification accuracy rate of 84% from a sample of approximately 2500 pieces
of 400 * 400 leather patches.
