Abstract
For applications, such as image recognition or scene understanding, we cannot process the whole image directly for the reason that it is inefficient and unpractical. Therefore, to reduce the complexity of the recognition of the image, segmentation is a necessary step. Image segmentation divides an image into several parts (regions) according to some local homogeneous features of the image. For this purpose, understanding of the features of the image is important. Features such as color, texture, and patterns are considered for segmentation. Therefore, the thrust of our work is on the extraction of color textural features from images. Color measurement is done in Gaussian color space and texture features are extracted with Gabor filters. The paper proposes image segmentation based on recursive splitting k-means method and experiments are focused on color natural images taken from Berkeley Segmentation Dataset (BSD).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/BSDS300/html/dataset/images.html
Aniyeva, S.: Color Differential Stucture. Image and Signal Processing (2007)
Bovik, A.C., Clark, M., Geisler, W.S.: Multichannel texture analysis using localized spatial filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(1), 55–73 (1990)
Elewa, A.T.M.: Morphometrics for nonmorphometricians. LNES , vol. 124. Springer (2010)
Fu, K.S., Mui, J.K.: A survey on image segmentation. Pattern Recognition 13, 3–16 (1981)
Gårding, J., Lindeberg, T.: Direct computation of shape cues using scale-adapted spatial derivative operators. International Journal of Computer Vision 17(2), 163–191 (1996)
Geusebroek, J.-M., van den Boomgaard, R., Smeulders, A.W.M., Dev, A.: Color and Scale: The Spatial Structure of Color Images. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 331–341. Springer, Heidelberg (2000)
Ho, P.-G.: Image segmentation. InTech (2011)
Ilea, D.E., Whelan, P.F.: Image segmentation based on the integration of colour-texture descriptors - a review. Pattern Recognition 44, 2479–2501 (2011)
Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using gabor filters. Pattern Recognition 24(12), 1167–1186 (1991)
Koenderink, J., Doorn, A.V.: Generic neighborhood operators. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(6), 597–605 (1992)
Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(8), 837–842 (1996)
Prasad, S.N., Domke, J.: Gabor filter visualization, http://www.cs.umd.edu/class/spring2005/cmsc838s/assignment-projects/gabor-filter-visualization/report.pdf
Ray, S., Turi, R.H.: Determination of number of clusters in k-means clustering and application in colour image segmentation. In: Proceedings of the 4th International Conference on Advances in Pattern Recognition and Digital Techniques (ICAPRDT 1999), pp. 137–143. Narosa Publishing House, New Delhi (1999)
ter Haar Romeny, B.M., Geusebroek, J.-M., Van Osta, P., van den Boomgaard, R., Koenderink, J.J.: Color Differential Structure. In: Kerckhove, M. (ed.) Scale-Space 2001. LNCS, vol. 2106, pp. 353–361. Springer, Heidelberg (2001)
Smith, L.I.: A tutorial on principal components analysis (2002), http://www.sccg.sk/~haladova/principal_components.pdf
Tsai, D.M., Lin, C.T.: The evaluation of normalized cross correlations for defect detection. Pattern Recognition Letters 24, 2525–2535 (2003)
Wang, H., Suter, D.: Color image segmentation using global information and local homogeneity. In: Proceedings of Seventh Conference on Digital Image Computing: Techniques and Applications, pp. 89–98 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kokil Kumar, C., Agarwal, A., Chillarige, R.R. (2012). Color and Texture Image Segmentation. In: Sombattheera, C., Loi, N.K., Wankar, R., Quan, T. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2012. Lecture Notes in Computer Science(), vol 7694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35455-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-35455-7_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35454-0
Online ISBN: 978-3-642-35455-7
eBook Packages: Computer ScienceComputer Science (R0)