International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.5, No.3, Mar. 2013

A Color-Texture Based Segmentation Method To Extract Object From Background

Full Text (PDF, 1249KB), PP.19-25

Views:123   Downloads:4


Saka Kezia, I. Santi Prabha, Vakulabharanam Vijaya Kumar

Index Terms

Color image segmentation, Morphology, OTSU Thresholding, GLCM


Extraction of flower regions from complex background is a difficult task, it is an important part of flower image retrieval, and recognition .Image segmentation denotes a process of partitioning an image into distinct regions. A large variety of different segmentation approaches for images have been developed. Image segmentation plays an important role in image analysis. According to several authors, segmentation terminates when the observer's goal is satisfied. For this reason, a unique method that can be applied to all possible cases does not yet exist. This paper studies the flower image segmentation in complex background. Based on the visual characteristics differences of the flower and the surrounding objects, the flower from different backgrounds are separated into a single set of flower image pixels. The segmentation methodology on flower images consists of five steps. Firstly, the original image of RGB space is transformed into Lab color space. In the second step 'a' component of Lab color space is extracted. Then segmentation by two-dimension OTSU of automatic threshold in 'a-channel' is performed. Based on the color segmentation result, and the texture differences between the background image and the required object, we extract the object by the gray level co-occurrence matrix for texture segmentation. The GLCMs essentially represent the joint probability of occurrence of grey-levels for pixels with a given spatial relationship in a defined region. Finally, the segmentation result is corrected by mathematical morphology methods. The algorithm was tested on plague image database and the results prove to be satisfactory. The algorithm was also tested on medical images for nucleus segmentation.

Cite This Paper

Saka Kezia, I. Santi Prabha, Vakulabharanam Vijaya Kumar,"A Color-Texture Based Segmentation Method To Extract Object From Background", IJIGSP, vol.5, no.3, pp.19-25, 2013.DOI: 10.5815/ijigsp.2013.03.03


[1]S. Belongie, et. al., "Color- and texture-based image segmentation using EM and its application to content-based image retrieval", Proc. of ICCV, p. 675-82, 1998.

[2]Y. Delignon, et. al., "Estimation of generalized mixtures and its application in image segmentation", IEEE Trans. on Image Processing, vol. 6, no. 10, p. 1364-76, 1997.

[3]D.K. Panjwani and G. Healey, "Markov random field models for unsupervised segmentation of textured color images", PAMI, vol. 17, no. 10, p. 939-54, 1995.

[4]J.-P. Wang, "Stochastic relaxation on partitions with connected components and its application to image segmentation", PAMI, vol. 20, no.6, p. 619-36, 1998.

[5]S.C. Zhu and A. Yuille, "Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation", PAMI, vol. 18, no. 9, p. 884-900.

[6]L. Shafarenko, M. Petrou, and J. Kittler, "Automatic watershed segmentation of randomly textured color images", IEEE Trans. on Image Processing, vol. 6, no. 11, p. 1530-44, 1997.

[7]W.Y. Ma and B.S. Manjunath, "Edge flow: a framework of boundary detection and image segmentation", Proc. of CVPR, pp 744-49, 1997.

[8]J. Shi and J. Malik, "Normalized cuts and image segmentation", Proc. of CVPR, p. 731-37, 1997.

[9]M. Borsotti, P. Campadelli, and R. Schettini, "Quantitative evaluation of color image segmentation results", Pattern Recognition letters, vol. 19, no. 8, p. 741-48, 1998.

[10]D. Comaniciu and P. Meer, "Robust analysis of feature spaces: color image segmentation", Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp 750-755, 1997.

[11]Das, M., Manmatha, R., and Riseman, E. M. 1999. Indexing flower patent images using domain knowledge. IEEE Intelligent systems, Vol. 14, No. 5, pp. 24-33.

[12]Nilsback, M. E. and Zisserman, A. 2004. Delving into the whorl of flower segmentation. In the Proceedings of British Machine Vision Conference, Vol. 1, pp. 27-30.

[13]Das, M., Manmatha, R., and Riseman, E. M. 1999. Indexing flower patent images using domain knowledge. IEEE Intelligent systems, Vol. 14, No. 5, pp. 24-33.

[14]Texture Features and KNN in Classification of Flower images. D S Guru, Y. H. Sharath, S. Manjunath RTIPPR,2010.

[15]Anisotropic Diffusion and Segmentation of Colored Flowers. Shoma Chatterjee, IEEE Sixth Indian Conference on Computer Vision, Graphics & Image Processing,2008.

[16]J. Weszka, C. Dyer, A. Rosenfeld, "A Comparative Study Of Texture Measures For Terrain Classification" IEEE Trans. SMC-6 (4), pp. 269-285, April 1976. 

[17]R.W. Conners, C.A. Harlow, "A Theoretical Comparison Of Texture Algorithms", IEEE Trans. PAMI-2, pp. 205-222, 1980.

[18]D.A. Clausi, M.E. Jernigan, "A fast method to determine co-occurrence texture features", IEEE Trans on Geoscience & Rem. Sens., vol. 36(1), pp. 298-300, 1998.

[19]D.A. Clausi, Yongping Zhao, "An advanced computational method to determine co-occurrence probability texture features", IEEE Int. Geoscience and Rem. Sens. Sym, vol. 4, pp. 2453-2455 2002.

[20]A.E. Svolos, A. Todd-Pokropek, "Time and space results of dynamic texture feature extraction in MR and CT image analysis", IEEE Trans. on Information Tech. in Biomedicine, vol. 2(2), pp. 48-54, 1998.

[21]F. Argenti, L. Alparone, G. Benelli, "Fast algorithms for texture analysis using co-occurrence matrices", IEE Proc on Radar and Signal Processing, vol. 137(6), pp.443-448, 1990.

[22]S. Kiranyaz, M. Gabbouj, "Hierarchical Cellular Tree: An Efficient Indexing Scheme for Content-Based Retrieval on Multimedia Databases", IEEE Transactions on Multimedia, vol. 9(1), pp. 102-119, 2007.

[23]A. Baraldi, F. Parmiggiani, "An Investigation Of The Textural Characteristics Associated With GLCM Matrix Statistical Parameters", IEEE Trans. on Geos. and Rem.Sens., vol. 33(2), pp. 293-304, 1995.

[24]R. Haralick, K. Shanmugam, I. Dinstein, "Texture Features For Image Classification", IEEE Transaction, SMC-3(6). Pp. 610-621, 1973.

[25]N. Otsu, "A Threshold Selection Method from Gray- Level Histogram", IEEE Trans. on System Man Cybernetics, vol. 9(I), pp. 62-66, 1979.

[26]P. Gong, J. D. Marceau, and P. J. Howarth, "A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data", Proc.Remote Sensing Environ, vol. 40, pp. 137-151, 1992.

[27]A. Ukovich, G. Impoco, G. Ramponi, "A tool based on the GLCM to measure the performance of dynamic range reduction algorithms", IEEE Int. Workshop on Imaging Sys. & Techniques, pp. 36-41, 2005.