Multimed Tools Appl DOI 10.1007/s11042-015-3041-3 Fabric defect inspection using prior knowledge guided least squares regression Junjie Cao 1 · Jie Zhang 1 · Zhijie Wen 2 · Nannan Wang 1 · Xiuping Liu 1 Received: 20 April 2015 / Revised: 30 July 2015 / Accepted: 22 October 2015 © Springer Science+Business Media New York 2015 Abstract This paper proposes an unsupervised model to inspect various detects in fabric images with diverse textures. A fabric image with defects is usually composed of a rela- tively consistent background texture and some sparse defects, which can be represented as a low-rank matrix plus a sparse matrix in a certain feature space. The process is formu- lated as a least squares regression based subspace segmentation model, which is convex, smooth and can be solved efficiently. A simple and effective prior is also learnt from local texture features of the image itself. Instead of considering only the feature space’ s global structure, the local prior is incorporated with it seamlessly by the proposed subspace segmentation model to guide and improve the segmentation. Experiments on a variety of fabric images demonstrate the effectiveness and robustness of the proposed method. Com- pared with existing methods, our method is more robust and locates various defects more precisely. Keywords Low-rank · Fabric defect detection · Prior knowledge · Least squares regression  Zhijie Wen wenzhijie@shu.edu.cn  Xiuping Liu xpliu@dlut.edu.cn Junjie Cao jjcao1231@gmail.com 1 School of Mathematical Sciences, Dalian University of Technology, Dalian, China 2 Department of Mathematics, College of Sciences, Shanghai University, Shanghai, China