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Automated Colorization of Grayscale Images Using Texture Descriptors and a Modified Fuzzy C-Means Clustering

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DOI: 10.4236/jilsa.2012.42013    4,786 Downloads   8,540 Views   Citations

ABSTRACT

A novel example-based process for Automated Colorization of grayscale images using Texture Descriptors (ACTD) without any human intervention is proposed. By analyzing a set of sample color images, coherent regions of homogeneous textures are extracted. A multi-channel filtering technique is used for texture-based image segmentation, combined with a modified Fuzzy C-means (FCM) clustering algorithm. This modified FCM clustering algorithm includes both the local spatial information from neighboring pixels, and the spatial Euclidian distance to the cluster’s center of gravity. For each area of interest, state-of-the-art texture descriptors are then computed and stored, along with corresponding color information. These texture descriptors and the color information are used for colorization of a grayscale image with similar textures. Given a grayscale image to be colorized, the segmentation and feature extraction processes are repeated. The texture descriptors are used to perform Content-Based Image Retrieval (CBIR). The colorization process is performed by Chroma replacement. This research finds numerous applications, ranging from classic film restoration and enhancement, to adding valuable information into medical and satellite imaging. Also, this can be used to enhance the detection of objects from x-ray images at the airports.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

C. Gauge and S. Sasi, "Automated Colorization of Grayscale Images Using Texture Descriptors and a Modified Fuzzy C-Means Clustering," Journal of Intelligent Learning Systems and Applications, Vol. 4 No. 2, 2012, pp. 135-143. doi: 10.4236/jilsa.2012.42013.

References

[1] A. Levin, D. Lischinski and Y. Weiss, “Colorization using optimization,” ACM Transactions on Graphics, Vol. 23, No. 3, 2004, pp. 689-694. doi:10.1145/1015706.1015780
[2] T. Welsh, M. Ashikhmin and K. Mueller, “Transferring Color to Greyscale Images,” ACM Transactions on Graphics, Vol. 21, No. 3, 2002, pp. 277-280. doi:10.1145/566570.566576
[3] X. Liu, L. Wan, Y. Qu, T. Wong, S. Lin, C. Leung and P. Heng, “Intrinsic Colorization,” ACM Transactions on Graphics, Vol. 27, No. 5, 2008, p. 152. doi:10.1145/1457515.1409105
[4] R. Irony, D. Cohen-Or and D. Lischinski, “Colorization by Example,” Proceedings of the Eurographics Symposium on Rendering, Konstanz, 29 June-1 July 2005, pp. 277-280.
[5] J. Malik and P. Perona, “Preattentive Texture Discrimination with Early Vision Mechanisms,” Journal of the Optical Society of America A, Vol. 7, No. 5, 1990, pp. 923-932. doi:10.1364/JOSAA.7.000923
[6] A. K. Jain and F. Farrokhnia, “Unsupervised Texture Segmentation Using Gabor Filters,” Pattern Recognition, Vol. 24, No. 12, 1991, pp. 1167-1186. doi:10.1016/0031-3203(91)90143-S
[7] S. Naotoshi, “Texture Segmentation Using Gabor Filters,” University of Maryland, College Park, 2006.
[8] X. Hu, X. Dong, J. Wu and P. Z. J. Dong, “Texture Segmentation Based on Probabilistic Index Maps,” Proceedings of the International Conference on Education Technology and Computer, Singapore, 17-20 April 2009, pp. 35-39. doi:10.1109/ICETC.2009.41
[9] X. Zhan, S. Xingbo and L. Yuerong, “Comparison of Two Gabor Texture Descriptor for Texture Classification,” Proceedings of the WASE International Conference on Information Engineering, Taiyuan, 10-11 July 2009, pp. 52-56. doi:10.1109/ICIE.2009.20
[10] M. Lux and S. A. Chatzichristofis, “LIRe: Lucene Image Retrieval—An Extensible Java CBIR Library,” Proceedings of the ACM International Conference on Multimedia, Vancouver, 27-31 October 2008, pp. 1085-1088. doi:10.1145/1459359.1459577
[11] S. Α. Chatzichristofis, Y. S. Boutalis and M. Lux, “IMG (Rummager): An Interactive Content Based Image Retrieval System,” Proceedings of the 2nd International Workshop on Similarity Search and Applications (SISAP), Prague, 29-30 August 2009, pp. 151-153. doi:10.1109/SISAP.2009.16
[12] S. Krinidis and V. Chatzis, “A Robust Fuzzy Local Information C-Means Clustering Algorithm,” IEEE Transactions on Image Processing, Vol. 19, No. 5, 2010, pp. 1328-1337. doi:10.1109/TIP.2010.2040763
[13] S. A. Chatzichristofis and Y. S.Boutalis, “Content Based Medical Image Indexing and Retrieval Using a Fuzzy Compact Composite Descriptor,” Proceedings of the 6th IASTED International Conference on Signal, Pattern Recognition and Applications, Innsbruck, 17-19 February 2009, pp. 1-6.
[14] C. Gauge and S. Sasi, “Automated Colorization of Grayscale Images Using Texture Descriptors,” Proceedings of the International Conference on Advances in Computer Science, Trivandrum, 21-22 December 2010, pp. 164166.
[15] C. Gauge and S. Sasi, “Modified Fuzzy C-Means Clustering Algorithm with Spatial Distance to Cluster Center of Gravity,” Proceedings of the 6th IEEE International Workshop on Multimedia Information and Retrieval, Taichung, 13-15 December 2010, pp. 308-313. doi:10.1109/ISM.2010.53

  
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