This probability density function is our content signature for the given image. Each Gaussian distribution function is defined by a mean vector and a covariance matrix. A general data clustering routine can provide clusters for which for the Gaussian parameters can be obtained. We use the k-means clustering algorithm followed by an optional cluster merging process. A mean vector and covariance matrix are computed for each of the resultant clusters, and the associated Gaussian distribution function is weighted by the number of elements in the corresponding cluster.
Any cluster having a singular covariance matrix is deleted and ignored in subsequent processing.
Global signatures are compared using an L2 distance measure, or an inner product similarity measure.