Abstract:
Screen-printing is an industry with a large number of applications ranging from
printing mobile phone logos to printing artworks on fabrics. In screen-printing,
image segmentation plays a critical role in deciding both the cost and the quality
attributes of printing. The work presented in this paper focuses on the development
of an approach to colour image segmentation using a combined approach of k-means
clustering and principal component analysis. The uncorrelated image data obtained
through a principal component analysis was clustered using a k-means clustering
algorithm. Since the study focused on the screen printing industry, the selection of
the number of clusters k which is the most critical element is allowed to be set
manually so that users can limit the number of colours to be segmented. It is shown
that this approach produces a significant improvement in colour image
segmentation. Results are also compared with another popular clustering algorithm
called the mean-shift, which is normally used in feature space clustering.
