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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Searching for relevant images given a query term is an important task in nowadays large-scale community databases. The image ranking approach presented in this work represents an image collection as a graph that is built using a multimodal similarity measure based on visual features and user tags. We perform a random walk on this graph to find the most common images. Further we discuss several scalability issues of the proposed approach and show how in this framework queries can be answered fast. Experimental results validate the effectiveness of the presented algorithm.

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When dealing with feedback from a human expert in a classification process, we usually think of obtaining the correct class label for an example. However, in many realworld settings, it may be much easier for the human expert to tell us to which classes the example does not belong. We propose a framework for this very practical setting to incorporate this kind of feedback. We demonstrate empirically that stable classification models can be built even in the case of partial not-label information and introduce a method to select useful training examples.