Goat Leather Quality Classification Using Computer Vision and Machine Learning

Goat leather is responsible for a large part of the income generated by the most diverse clothing products, but the lack of modernization of some stages of leather production is still very evident, and may lead to divergent opinions regarding the quality of leather among tanning industries and finishing industries. In this paper it is proposed a new approach to aid goat leather qualification specialists based on the position of the found failures in the goat leather. There are two steps in the proposed approach. The first one is find the failure regions and the last one is extract some feature from the failure map founded. In this paper the authors use Gray Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP) and Structural Co-occurrence Matrix (SCM) as feature extractors from leather images. The classification task is executed by kNearest Neighbors (KNN), Multi Layer Perceptron (MLP) and Support Vector Machine (SVM). The combination of the LBP attribute extractor with the MLP classifier has 90% accuracy rates for the classification of regions with failure, but for quality classification of the leather, the SVM classifier has the best results (86% of accuracy rate), also using LBP. The results show that the proposed approach can be used to aid the specialists to classify the quality of the goat leather.