Learning a Class-specific and Shared Dictionary for Classifying Surface Defects of Steel Sheet

Abstract

An approach to a class-specific and shared dictionary learning (CDSDL) for sparse representation is proposed to classify surface defects of steel sheet. The proposed CDSDL algorithm is modelled as a unified objective function, covering reconstructive error, sparse and discriminative promotion constraints. With the high-quality dictionary, the compact, reconstructive and discriminative feature representation of an image can be extracted. Then the classification can be efficiently performed by discriminative information obtained from the reconstructive error or the sparse vector. Based on a dataset of surface images captured from a practical steel production line, the CDSDL algorithm is carried out to verify its effectiveness. Experimental results indicate that the CDSDL algorithm is more effective in classifying surface defects of steel sheet than other algorithms.