Synthetic Fiber Image Segmentation using a Cooperative System of Local Hill Climbing Optimization and K-Means Clustering

The proposed method explains the segmentation of synthetic fiber images based on the cooperative approach of local hill climbing and k means clustering. In this work, RGB image is transformed into CIELch space for the efficient extraction of the hidden treasure in the images. The combined approach of local optimization search technique, HC and KMC is applied for the segmentation of synthetic fiber images. This color histogram based technique works on the principle of identification of peaks in the color histogram of the satellite image. The identified peaks are considered as initial seed or clusters. These seeds are then applied to the KMC algorithm to perform the final segmentation. The combined approach of HC and KMC had provided the best result for less complexity images.