6.6 Comparison of segmentation results on Baboon image

As an example of the present experiments for image segmentation, segmentation results for Baboon image for 10 clusters are presented here. These results are generated by the above proposed hybrid clustering algorithms along with the standard K-Means and standard FCM algorithms.

Segmenting Images Using Hybridization of K-Means and Fuzzy C-Means Algorithms DOI: http://dx.doi.org/10.5772/intechopen.86374

Figure 17.

Image segmentation results for Baboon image (for 10 Clusters).

For segmentation, here, each algorithm is executed using Baboon image data assuming that the number of clusters is 10, i.e., k = 10. Each segment is represented by each cluster. Separate color code is assigned to each cluster. The color codes are red, yellow, green, blue, orange, black, white, gray, cyan and magenta. The projections of all segmentation results generated by the algorithms are shown in Figure 17. The original Baboon image also shown in the figure.

In all the experiments, it is observed that hybrid clustering algorithm KMandFCM is showing better performance in terms of CPU, clustering fitness and SSE than the other algorithms.
