Image clustering using higher-order statistics

Traditional algorithms for clustering image data have used Euclidean or Mahalanobis distance. Here, a more general higher-order statistics-based closeness measure derived from a series expansion for a multivariate probability density function in terms of the Gaussian function and the Hermite polynomials is proposed for clustering. The superiority of this measure is demonstrated with an example application.