Improved Mean Shift Spectral Clustering Based on Reduced Set Density Estimator
Mean shift spectral clustering (MSSC) brings us an alternative for image segmentation. However, owing to being based on the classical Parzen window estimator (PW) and employing the full data sample for density estimation, the usefulness of MSSC is weakened. In this paper, the improved mean shift spectral clustering (IMSSC) algorithm is proposed by replacing PW with the reduced set density estimator (RSDE). Due to just a few sample points in the reduced set being referred to, the time complexity of mean shift embedded in IMSSC decreases to O(mN) and the total computational costs of IMSSC are sharply reduced.