Content-Based Image Retrieval for Surface Defects of Hot Rolled Steel Strip Using Wavelet-Based LBP

Quality control by artificial vision with applications in industrial manufacturing is a challenging task due to the significant variability of surface defects. In this work, we propose to use content based image retrieval CBIR to manage the large data produced by surface inspection systems. The performance of the CBIR system was evaluated using textural features extracted from NEU database that collects six kinds of surface defects of the hot-rolled steel strip. Different similarity measurements were used to retrieve the most similar images to the query image. The effectiveness of Wavelet based local binary patterns WLBP features was shown in the experimental results for the retrieval of surface defects. WLBP features using Chi square distance achieved the highest retrieval values compared to LBP, GLCM, and EHD features.