The week's pick
Random Articles
Reseach Article
Image Segmentation with Texture Gradient and Spectral Clustering
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 61 - Number 11 |
| Year of Publication: 2013 |
| Authors: Indu V Nair, Kumari Roshni V. S. |
10.5120/9972-4800
|
Indu V Nair, Kumari Roshni V. S. . Image Segmentation with Texture Gradient and Spectral Clustering. International Journal of Computer Applications. 61, 11 ( January 2013), 19-26. DOI=10.5120/9972-4800
Abstract
For some applications the whole image cannot be processed directly because it is inefficient and impractical. Segmentation results in a set of images that cover the entire image. This work proposes a two stage segmentation method, which effectively process both the textured and non-textured regions. Dual Tree Complex Wavelet Transform, an extension of discrete wavelet transform, extracts texture feature from the image and orientation median filtering reduces the double edge effect at the texture edges. Watershed transform of Gaussian gradient of combined texture and non-texture feature give the first stage segmentation. The initial segmentation into super-pixels reduces computational burden and the second stage uses spectral clustering technique to cluster these primitive regions.
References
- Robert J. O'Callaghan, David R. Bull, "Combined Morphological-Spectral Unsupervised Image Segmentation", IEEE Transactions on Image Processing, Vol. 14, 2005, pp. 49-62.
- P. Hill, C. Canagarajah, and D. Bull, "Image Segmentation Using a Texture Gradient-Based Watershed Transform," IEEE Trans. Image Process. vol. 12, no. 12, pp. 1618–1633, Dec. 2003.
- Anil K. Jain, Farshid Farrokhnia ''Unsupervised Texture Segmentation Using Gabor Filters'',1990. IEEE Trans . on Image Processing.
- Chang T. and Kuo, C. C. J. , 1993, "Texture Analysis and Classification with Tree-Structured Wavelet, Transform," IEEE Trans. on Image Processing.
- Manesh Kokare, B. N. Chatterji and P. K. Biswas, "Wavelet Transform Based Texture Features For Content Based Image Retrieval" ,Electronics and Electrical Communication Engineering Department, Indian Institute of Technology, Kharagpur.
- Zhi Jin Wang, "Fast Image (Information) Retrieval (IR) Using Wavelet Coding" Spring 2006,Department of Electrical and Computer Engineering, San Diego State University.
- N. Kingsbury, "Complex Wavelets for Shift Invariant Analysis and Filtering of Signals," J. Appl. Comput. Harmonic Anal. , vol. 10, no. 3, pp. 234–253, May 2001.
- Musoko Victor, Proch´azka Ale, "Complex Wavelet Transform in Signal and Image Analysis", Institute of Chemical Technology, Department of Computing and Control Engineering ,Technicka.
- Patrenahalli M. Narendra, "A Separable Median Filter for Image Noise Smoothing'' IEEE Transactions on Pattern analysis and machine intelligence. Vol. Pami-3,No. 1, January 1981.
- Angeles Hernandez-Carrascal, Slawomir J. Nasuto ,"Application of Gaussian Multi-scale Representation to Feature Tracking in Meteorological Satellite Imagery'' , 2010 EUMETSAT Meteorological Satellite Conference, Cordoba, Spain, 20-24 September 2010.
- Jos B. T. M. Roerdink and Arnold Meijster "The Watershed Transform: Definitions, Algorithms and Parallelization Strategies", Fundamenta Informaticae 41 (2001) IOS Press.
- L. Vincent and P. Soille, "Watersheds in Digital Spaces: an Efficient Algorithm Based on Immersion Simulations," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 13, no. 6, pp. 583–598, Jun. 1991.
- S. Beucher and F. Meyer, "The morphological approach to segmentation: The watershed transformation". In Mathematical Morphology in Image Processing'' (E. R. Dougherty, Ed. ), pp. 433_481, Dekker, New York, 1993.
- J. Ohser, K. Schladitz "Image Processing and Analysis'', Clarendon press. Oxford 2006.
- Haralick, R. M. 1979. "Statistical and Structural Approaches to Texture" Proceedings of the IEEE, Vol. 67 pp. 786-804.
- Clausi, D. A. 2002. "An Analysis of Co-occurrence Texture Statistics as a Function of Grey-Level Quantization" Canadian Journal of Remote Sensing vol. 28 no. 1 pp. 45-62 .
- Y. Weiss, "Segmentation Using Eigenvectors: A Unifying View," in Proc. International Conference on Computer Vision, vol. 2, 1999, pp. 975–982.
- J. Shi and J. Malik, "Normalized Cuts and Image Segmentation," IEEE Trans. Pattern Anal. Machine Intell. , vol. 22, no. 8, pp. 888–905, Aug. 2000.
- C. Ding, X. He, H. Zha, M. Gu, and H. Simon, "A min-max Cut Algorithm for Graph Partitioning and Data Clustering," in Proc. Int. Conf. Data Mining, 2001, pp. 107–114.
- Ulrike von Luxburg," A Tutorial on Spectral Clustering", Max Planck Institute for Biological Cybernetics, T¨ubingen, Germany Statistics and Computing, 17 (4), 2007.
- F. Tung et al. "Enabling Scalable Spectral Clustering for Image Segmentation" , Pattern Recognition 43 (2010) 4069–4076 .
- Marina Meila, Jianbo Shi, "A Random Walks View of Spectral Segmentation", In Proceedings of International Conference on AI and Statistics, 2001.
- N. K. C. W. Shaffrey and I. Jermyn, "Unsupervised Image Segmentation via Markov Trees and Complex Wavelets," in Proc. Int. Conf. Image Processing, vol. 3, Sep. 2002, pp. 801–804.
- P. Hill, C. Canagarajah, and D. Bull, "Texture Gradient Based Watershed Segmentation," in Proc. Int. Conf. Acoustics, Speech and Signal Processing, vol. 4, 2002, pp. 3381–3384.
Index Terms
Keywords