A Neural Approach for Color-Textured Images Segmentation

In this paper, we present a neural approach for
unsupervised natural color-texture image segmentation, which is
based on both Kohonen maps and mathematical morphology, using
a combination of the texture and the image color information of the
image, namely, the fractal features based on fractal dimension are
selected to present the information texture, and the color features
presented in RGB color space. These features are then used to train
the network Kohonen, which will be represented by the underlying
probability density function, the segmentation of this map is made
by morphological watershed transformation. The performance of our
color-texture segmentation approach is compared first, to color-based
methods or texture-based methods only, and then to k-means method.

Authors:

• Khalid Salhi
• El Miloud Jaara
• Mohammed Talibi Alaoui

Keywords:

• Segmentation
• color-texture
• neural networks
• fractal
• watershed.

References:

[1] B. B. Chaudhuri and N. Sarkar, “Texture segmentation using
fractal dimension,” Pattern Analysis and Machine Intelligence, IEEE
Transactions on, vol. 17, no. 1, pp. 72–77, 1995.
[2] N. Sarkar and B. Chaudhuri, “An efficient differential box-counting
approach to compute fractal dimension of image,” Systems, Man and
Cybernetics, IEEE Transactions on, vol. 24, no. 1, pp. 115–120, 1994.
[3] T. Kohonen, Self-organization and associative memory, vol. 8. Springer
Science & Business Media, 2012.
[4] E. Parzen, “On estimation of a probability density function and mode,”
The annals of mathematical statistics, vol. 33, no. 3, pp. 1065–1076,
1962.
[5] S. Beucher, “Segmentation tools in mathematical morphology,” in San
Diego’90, 8-13 July, pp. 70–84, International Society for Optics and
Photonics, 1990.
[6] M. Talibi-Alaoui and A. Sbihi, “Application of a mathematical
morphological process and neural network for unsupervised texture image
classification with fractal features,” IAENG International Journal of
Computer Science, vol. 39, no. 3, pp. 286–294, 2012.
[7] T. Ojala, T. M¨aenp¨a¨a, M. Pietikainen, J. Viertola, J. Kyll¨onen, and
S. Huovinen, “Outex-new framework for empirical evaluation of texture
analysis algorithms,” in Pattern Recognition, 2002. Proceedings. 16th
International Conference on, vol. 1, pp. 701–706, IEEE, 2002.