Assessment of Intermittent Leather Based on Image Score Pattern

The process of intermittent leather inspection is being predominantly carried out with the support of human intervention based on homogenous distribution of colors. However, results of the observations between one experts to another expert may be different in opinion. Therefore, to emphasis some sort of supporting hand to the experts while taking decision, the authors have introduced an algorithm based on Image Score Pattern to distinguish between defect versus non-defect intermittent leather images. About 32 features generated from Gray Level Co-occurrence Matrix, Simple Linear Iterative Clustering and Minimum Spanning Tree Clustering from the training and testing datasets of about 1132 and 404 generated. The results of the classifier Support Vector Machine has confirmed the accuracy of 84% for the proposed Image Score Pattern method for these datasets. Similarly, other performance measures such as Precision, Recall, F1-Score, Specificity and Error Rate are also confirming that proposed method is performing in aligning of intermittent leather.