Illumination-insensitive texture discrimination based on illumination compensation and enhancement Muwei Jian a , Kin-Man Lam a,⇑ , Junyu Dong b a Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong b Department of Computer Science, Ocean University of China, Qingdao, China article info Article history: Received 14 December 2012 Received in revised form 3 December 2013 Accepted 12 January 2014 Available online 21 January 2014 Keywords: Illumination compensation Illumination enhancement Illumination-effect matrix Illumination-insensitive texture abstract As the appearance of a 3D surface texture is strongly dependent on the illumination direc- tion, 3D surface-texture classification methods need to employ multiple training images captured under a variety of illumination conditions for each class. Texture images under different illumination conditions and directions still present a challenge for texture-image retrieval and classification. This paper proposes an efficient method for illumination-insen- sitive texture discrimination based on illumination compensation and enhancement. Fea- tures extracted from an illumination-compensated or -enhanced texture are insensitive to illumination variation; this can improve the performance for texture classification. The proposed scheme learns the average illumination-effect matrix for image representation under changing illumination so as to compensate or enhance images and to eliminate the effect of different and uneven illuminations while retaining the intrinsic properties of the surfaces. The advantage of our method is that the assumption of a single-point light source is not required, so it circumvents and overcomes the limitations of the Lambertian model and is also suitable for outdoor settings. We use a wide range of textures in the Pho- Tex database in our experiments to evaluate the performance of the proposed method. Experimental results demonstrate the effectiveness of our proposed methods. Ó 2014 Elsevier Inc. All rights reserved. 1. Introduction The appearance of rough surface textures may be dramatically different when they are lit from different directions. For example, Fig. 1 shows images of the same surface texture captured under varied lighting directions. They look dissimilar mainly due to the different illumination directions. Although it is well known that the appearance of a texture is strongly dependent on the illumination directions, dealing with illumination-insensitive texture is still an open issue and worth fur- ther investigation [8,9,37,39]. As a special type of image, texture can describe a wide variety of surface characteristics. Texture is very important for human visual perception and plays a key role in computer vision and pattern recognition. In addition, since texture can be effectively used for characterizing image regions, texture features have been extensively studied in image classification and content-based image retrieval, as well as in other fields related to pattern analysis [8,9,21,22,38]. Traditionally, texture-representation methods can be divided into three categories, namely structural [34], statistical [17,26], and multi-resolution filtering methods [14,19,24,27–29,36]. These methods have been effectively used for texture analysis, segmentation, retrieval and classification [1,8]. However, most of these previous methods focus on texture-feature 0020-0255/$ - see front matter Ó 2014 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.ins.2014.01.019 ⇑ Corresponding author. Tel.: +852 2766 6207. E-mail addresses: 10902666r@polyu.edu.hk (M. Jian), enkmlam@polyu.edu.hk (K.-M. Lam), dongjunyu@ouc.edu.cn (J. Dong). Information Sciences 269 (2014) 60–72 Contents lists available at ScienceDirect Information Sciences journal homepage: www.elsevier.com/locate/ins