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In this paper we propose a framework to address the reassembly of shredded documents. Inspired by the way humans approach this problem we introduce a novel algorithm that iteratively determines groups of fragments that fit together well. We identify such groups by evaluating a set of constraints that takes into account shape-and content-based information of each fragment. Accordingly, we choose the best matching groups of fragments during each iteration and implicitly determine a maximum spanning tree of a graph that represents alignments between the individual fragments. After each iteration we update the graph with respect to additional contextual knowledge. We evaluate the effectiveness of our approach on a dataset of 16 fragmented pages with strongly varying content. The robustness of the proposed algorithm is finally shown in situations in which material is lost.

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This paper presents a method for creating a discriminative color model for a given object class based on color occurrence statistics. A discriminative color model can be used to classify individual pixels of images with regards to whether they may belong to the wanted object. However, in contrast to existing approaches, we do not exploit pixel-wise object annotations but only global negative and positive image labels. Therefore our approach requires significantly less manual effort. We quantitatively evaluate the performance of our approach on two publicly available datasets and compare it to a baseline approach, which utilizes pixel annotations. The experimental results show that our approach is on par with pixel-wise approaches although requiring only a single global image label.

Abstract. In this work we present a feature bundling technique that aggregates individual local features with features from their spatial neighborhood into bundles. The resulting bundles carry more information of the underlying image content than single visual words. As in practice an exact search for such bundles is infeasible, we employ a robust approximate similarity search with min-hashing in order to retrieve images containing similar bundles. We demonstrate the benefits of these bundles for small object retrieval, i.e. logo recognition, and generic image retrieval. Multiple bundling strategies are explored and thoroughly evaluated on three different datasets.