Recoloring textile fabric images based on improved fuzzy clustering

This article proposes a new recoloring method for textile fabric images based on improved fuzzy local information c-means (FLICM) clustering. In the clustering algorithm, the fuzzy factor was modified so that it can produce reliable segmentation in areas with rich details. With the obtained cluster labels and pixel-wise memberships, the color of each pixel is modeled as the linear combination of the two most dominant colors. The recoloring process was then conducted by replacing the specified dominant color with user-provided target colors. Experimental results showed that the proposed method can produce natural and faithful color appearance on both printed and yarn-dyed fabric images, and outperforms the state-of-the-art. © 2016 Wiley Periodicals, Inc. Col Res Appl, 2016

[1]  Chi-Keung Tang,et al.  Local color transfer via probabilistic segmentation by expectation-maximization , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[2]  Mariano Rivera,et al.  Computing the α-channel with probabilistic segmentation for image colorization , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[3]  Daoqiang Zhang,et al.  Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation , 2007, Pattern Recognit..

[4]  Xin Du,et al.  Channel selection for multispectral color imaging using binary differential evolution. , 2014, Applied optics.

[5]  Xiangchu Feng,et al.  Fuzzy region competition-based auto-color-theme design for textile images , 2013 .

[6]  Stephen DiVerdi,et al.  Palette-based photo recoloring , 2015, ACM Trans. Graph..

[7]  Xi Chen,et al.  A Spatial Clustering Method With Edge Weighting for Image Segmentation , 2013, IEEE Geoscience and Remote Sensing Letters.

[8]  Paul Isaac On injective L-modules , 2005, Int. J. Math. Math. Sci..

[9]  Maoguo Gong,et al.  Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation , 2013, IEEE Transactions on Image Processing.

[10]  Tao Tang,et al.  A Kernel Clustering Algorithm With Fuzzy Factor: Application to SAR Image Segmentation , 2014, IEEE Geoscience and Remote Sensing Letters.

[11]  Chi-Keung Tang,et al.  Soft Color Segmentation and Its Applications , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Li Xu,et al.  A sparse control model for image and video editing , 2013, ACM Trans. Graph..

[13]  Jean-Noël Vittaut,et al.  Segmented Images Colorization Using Harmony , 2010, 2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems.

[14]  Bernhard Schölkopf,et al.  Automatic Image Colorization Via Multimodal Predictions , 2008, ECCV.

[15]  Eli Saber,et al.  Survey of contemporary trends in color image segmentation , 2012, J. Electronic Imaging.

[16]  Xiaowu Chen,et al.  Manifold preserving edit propagation , 2012, ACM Trans. Graph..

[17]  Dani Lischinski,et al.  Colorization using optimization , 2004, ACM Trans. Graph..

[18]  Aly A. Farag,et al.  A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data , 2002, IEEE Transactions on Medical Imaging.

[19]  Dejun Zheng A novel method for fabric color transfer , 2015 .

[20]  Erik Reinhard,et al.  Color Transfer between Images , 2001, IEEE Computer Graphics and Applications.

[21]  Ho-Jin Choi,et al.  A randomized algorithm for natural object colorization , 2014, Comput. Graph. Forum.

[22]  Daoqiang Zhang,et al.  Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[23]  Michael H. Brill,et al.  Note calibrating low-scattering samples using Kubelka–Munk model , 2016 .

[24]  Kwan H. Lee,et al.  Local color transfer between images using dominant colors , 2013, J. Electronic Imaging.

[25]  Pat Hanrahan,et al.  Probabilistic color-by-numbers , 2013, ACM Trans. Graph..

[26]  Stelios Krinidis,et al.  A Robust Fuzzy Local Information C-Means Clustering Algorithm , 2010, IEEE Transactions on Image Processing.