A novel method combining the characteristics of structure and region information is proposed for automatic segmentation of the color region for different kinds of fabrics. For improving image quality for computer analysis, the structure-texture decomposition processing has been used to extract the main structure from the fabric image, where the fine structure details of fabric yarn patterns have been removed. By using the CIE-Lab color system, the color structure image is then segmented by a fuzzy region-based segmentation model that can be solved efficiently through a fast numerical scheme. The experimental results show that the main disadvantage and difficulty of using color clustering-based methods and commonly used image segmentation methods for fabric color separation is overcome by the proposed method. The proposed method has high accuracy and the computation time is very reasonable. It can be applied to extract fabric color regions for different fabric structures, such as woven, knitted and embroidery structures.
LinJ. Applying a co-occurrence matrix to automatic inspection of weaving density for woven fabrics. Text Res J2002; 72: 486–490.
5.
KuoCKaoC. Self-organization map network for automatically recognizing color texture fabric nature. Fibers Polym2007; 8: 174–180.
6.
XinBHuJBaciuG. Investigation on classification of weave pattern based on an active grid model. Text Res J2009; 79: 1123–1134.
7.
PanRGaoWLiuJ. Automatic detection of the layout of color yarns for yarn-dyed fabric via a FCM algorithm. Text Res J2010; 80: 1222–1231.
8.
ZhengDHanYHuJ. A new method for classification of woven structure for yarn-dyed fabric. Text Res J2014; 84: 78–95.
9.
KuoCJianBWuH. Automatic machine embroidery image color analysis system. Part I: Using Gustafson-Kessel clustering algorithm in embroidery fabric color separation. Text Res J2012; 82: 571–583.
10.
KuoCTsaiC. Automatic recognition of fabric nature by using the approach of texture analysis. Text Res J2006; 76: 375–381.
11.
PanRLiuJGaoW. Measuring linear density of threads in single-system-mélange color fabrics with FCM algorithm. Color Res Appl2012; 38: 456–462.
12.
Zheng D, Han Y, Baciu G, et al. Design through cognitive color theme: a new approach for fabric design. In: 11th IEEE international conference on cognitive informatics & cognitive computing, 2012, pp.346-355.
13.
HanYZhengDBaciuG. Fuzzy region competition-based auto-color-theme design for textile images. Text Res J2013; 83: 638–650.
14.
ShafarenkoLPetrouMKittlerJ. Histogram-based segmentation in a perceptually uniform color space. IEEE Trans Image Process1998; 7: 1354–1358.
15.
MacQueenJSome methods for classification and analysis of multivariate observations. In: LeCamLNeymanJ (eds). proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Berkeley, CA: University of California Press, 1967, pp. 281–297.
16.
KaufmanLRousseeuwP. Finding groups in data: An introduction to cluster analysis, New York: Wiley, 1990.
17.
KuoCShihCLeeJ. Automatic recognition of fabric weave patterns by a fuzzy c-means clustering method. Text Res J2004; 74: 107–111.
18.
FurferiRGoverniL. The recycling of wool clothes: An artificial neural network colour classification tool. Int J Adv Manuf Technol2008; 37: 722–731.
19.
ZhengD. A new method for the detection and classification of weave pattern repeat. Text Res J2014; 84: 1586–1599.
20.
AujolJGilboaGChanT. Structure-texture image decomposition—modeling, algorithms, and parameter selection. Int J Comput Vision2006; 67: 111–136.
21.
XuLYanQXiaY. Structure extraction from texture via relative total variation. ACM Trans Graph2012; 31: 1310–1391.
22.
LevinALischinskiDWeissY. Colorization using optimization. ACM Trans Graph2004; 23: 689–694.
23.
KrishnanDSzeliskiR. Multigrid and multilevel preconditioners for computational photography. ACM Trans Graph2011; 30: 177–177.
24.
HuangYNgMWenY. A fast total variation minimization method for image restoration. SIAM J Multiscale Model Simulat2008; 7: 774–795.
25.
LiFNgMZengT. A multiphase image segmentation method based on fuzzy region competition. SIAM J Imag Sci2010; 3: 277–299.
26.
ChambolleALionsP. Image recovery via total variation minimization and related problems. Numer Math1997; 76: 167–188.
27.
ChanTVeseL. Active contour without edges. IEEE Trans Image Process2001; 10: 266–277.
28.
ChambolleA. An algorithm for total variation minimization and applications. J Math Imaging Vision2004; 20: 89–97.
29.
HanYWangWFengX. A fast multiphase image segmentation algorithm based on nonconvex regularizer. Pattern Recognit2012; 45: 363–372.
30.
HanYFengXBaciuG. Variational and PCA based natural image segmentation. Pattern Recognit2013; 46: 1971–1984.
ComaniciuDMeerP. Mean shift: A robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell2002; 24: 603–619.
33.
Yang A, Wright J, Sastry S, et al. Unsupervised segmentation of natural images via lossy data compression. EECS Department, University of California, Berkeley, 2006, pp.1--22.
34.
Rao S, Mobahi H, Yang A, et al. Natural image segmentation with adaptive texture and boundary encoding (Lecture Notes in Computer Science, vol. 5994). New York: Springer, 2009; pp. 135--146.
35.
ChenSCaoLWangY. Image segmentation by MAP-ML estimations. IEEE Trans Image Process2010; 19: 2254–2264.
36.
Tomasi C and Manduchi R. Bilateral filtering for gray and color images. In: IEEE sixth international conference on computer vision, 1998, pp. 839-846.
37.
CarsonCBelongieSGreenspanH. Blobworld: Image segmentation using expectation-maximization and its application to image querying. IEEE Trans Pattern Anal Mach Intell2002; 24: 1026–1038.
38.
KeysR. Cubic convolution interpolation for digital image processing. IEEE Trans Signal Proc Acoustics Speech Signal Proc1981; 29: 1153–1160.
