An Effective Segmentation Pattern Using Multi-class Independent Component Analysis on High Quality Color Texture Images
Balakrishnan Natarajan, S. P. Shantharajah · Research Journal of Applied Sciences Engineering and Technology · 2016
An efficient segmentation pattern proposes to improve the efficiency of segmentation through Multi-Class Independent Component InfoMax Analysis (MICIA) on multi-class high-quality color images. To attain richer segmentation of color, textures with minimal computation time, MICIA combines the watershed cuts principle and Minimal Spanning Forest method. The higher quality texture image is segmented by using the watershed cuts principle. Watershed cuts principle in MICIA is associated with regional minima of the map to handle multi-class poorly defined boundary images. Independent Component Analysis (ICA) is based on InfoMax which achieves richer segmentation of color textures with maximum likelihood function. ICA is based on InfoMax. It handles multi-class texture images. Because of the ICA maximum likelihood ensures higher independence on segmentation cuts.This produces an effective segmentation which can be used to improve the appearance of the high-quality images. To prove the efficiency, the experiment is conducted on factors such as sub-pixel accuracy rate on segmenting, multi-class image segmentation time and true positive rate.