Automated feature weighting and random pixel sampling in k-means clustering for terahertz image segmentation

Terahertz (THz) imaging is an innovative technology of imaging which can supply a large amount of data unavailable through other sensors. However, the higher dimension of THz images can be a hurdle to their display, their analysis and their interpretation. In this study, we propose a weighted feature space and a simple random sampling in k-means clustering for THz image segmentation. Our approach consists to estimate the expected centers, select the relevant features and their scores, and classify the observed pixels of THz images. It is more appropriate for achieving the best compactness inside clusters, the best discrimination of features, and the best tradeoff between the clustering accuracy and the low computational cost. Our approach of segmentation is evaluated by measuring performances and appraised by a comparison with some related works.