Feature grouping by 'relocalisation' of eigenvectors of the proximity matrix

We describe a widely applicable method of grouping or clustering image features (such as points, lines, corners, flow vectors and the like). It takes as input a "proximity matrix" H a square, symmetric matrix of dimension N (where N is the number of features). The element i,j of H is an initial estimate of the "proximity" between the ith and yth features. As output it delivers another square symmetric matrix S whose i-)th element is near to, or much less than unity according as features i and j are to be assigned to the same or different clusters.