Abstract:
Image segmentation is a process of partitioning a digital image into smaller parts or
small region to highlight the much important parts of an image. Segmented parts of
an image should possess similar properties such as intensity, texture, color etc.
Spectral clustering methods are based on eigenvectors of Laplacian matrices
associated with the graphs. In this study, we considered a digital image as a graph
and used various existing clustering methods to find the segmentations. Second
smallest eigenvector of generalized eigensystem, the recursive two way normalized
cut method, simultaneous k-way cut with multiple eigenvectors and k-means
algorithms are used to partition the images. We compare the clusters obtained from
these methods and identify the most efficient method in order to classify the images
we considered. Calinski – Harabasz measure and gap evaluation criterion are used to
evaluate the quality of clusters. Simulations are carried out using Matlab.
