We present a novel approach to automatically find spatial configurations of local features occurring frequently on instances of a given object class, and rarely on the background....
Till Quack, Vittorio Ferrari, Bastian Leibe, Luc J...
Abstract. We present a method for mining frequently occurring objects and scenes from videos. Object candidates are detected by finding recurring spatial arrangements of affine cov...
In this work we study the mining of top-K frequent closed itemsets, a recently proposed variant of the classical problem of mining frequent closed itemsets where the support thresh...
Abstract. In this paper, we present a FASST mining approach to extract the frequently changing semantic structures (FASSTs), which are a subset of semantic substructures that chang...
We propose an efficient algorithm for mining frequent approximate sequential patterns under the Hamming distance model. Our algorithm gains its efficiency by adopting a "brea...