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This paper addresses the problem of finding correspondences between two sets of features by using multiple order constraints all together. First, we build a high-order supersymmetric tensor, called multiple order tensor, to incorporate the constraints of different orders (e.g., unary, pairwise, third order, etc.). The multiple order tensor naturally merges multi-granularity geometric affinities, thus it presents stronger descriptive power of consistent constraints than the individual order based methods. Second, to achieve the optimal matching, we present an efficient computational approach for the power iteration of the multiple order tensor. It only needs sparse tensor elements and reduces the sampling size of feature tuples, due to the supersymmetry of the multiple order tensor. The experiments on both synthetic and real image data show that our approach improves the matching performance compared to state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In order to mine spatial-temporal clusters from geo-databases, two clustering methods with close relationships are proposed, which are both based on neighborhood searching strategy, and rely on the sorted k-dist graph to automatically specify their respective algorithm arguments. We declare the most distinguishing advantage of our clustering methods is they avoid calculating the spatial-temporal distance between patterns which is a tough job. Our methods are validated with the successful extraction of seismic sequence from seismic databases, which is a typical example of spatial-temporal clusters. [ABSTRACT FROM AUTHOR]