Fabian Richter

Bio: Fabian Richter is an academic researcher from University of Augsburg. The author has contributed to research in topics: Graph (abstract data type) & Structured support vector machine. The author has an hindex of 6, co-authored 12 publications receiving 108 citations. Previous affiliations of Fabian Richter include Augsburg College.

Learning to Reassemble Shredded Documents

Abstract: In this paper, we address the problem of automatically assembling shredded documents. We propose a two-step algorithmic framework. First, we digitize each fragment of a given document and extract shape- and content-based local features. Based on these multimodal features, we identify pairs of corresponding points on all pairs of fragments using an SVM classifier. Each pair is considered a point of attachment for aligning the respective fragments. In order to restore the layout of the document, we create a document graph in which nodes represent fragments and edges correspond to alignments. We assign weights to the edges by evaluating the alignments using a set of inter-fragment constraints which take into account shape- and content-based information. Finally, we use an iterative algorithm that chooses the edge having the highest weight during each iteration. However, since selecting edges corresponds to combining groups of fragments and thus provides new evidence, we reevaluate the edge weights after each iteration. We quantitatively evaluate the effectiveness of our approach by conducting experiments on a novel dataset. It comprises a total of 120 pages taken from two magazines which have been shredded and annotated manually. We thus provide the means for a quantitative evaluation of assembly algorithms which, to the best of our knowledge, has not been done before.

Multimodal ranking for image search on community databases

Abstract: 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.

A graph algorithmic framework for the assembly of shredded documents

Abstract: 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.

Partial contour matching for document pieces with content-based prior

Abstract: In this paper we present a method for aligning shredded document pieces based on outer contours and content-based prior information. Our approach relies on domain-specific knowledge that document pieces must complement each other when aligned correctly. Building on this intuition we propose a variant of MSAC (M-estimator SAmple Consensus) to estimate an hypothesis that recovers the spatial relationship between pairs of pieces. To do so we first approximate their boundaries by polygons from which we define consensus sets between fragments. Each consensus set provides multiple hypotheses for aligning one piece onto the other. An optimal hypothesis is identified by applying a two-stage procedure in which we discard locally inconsistent hypotheses before verifying the remainder for global consistency.

Leveraging community metadata for multimodal image ranking

Abstract: 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.

Advances In Kernel Methods Support Vector Learning

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Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement, and Retrieval

Abstract: Where previous reviews on content-based image retrieval emphasize what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image A comprehensive treatise of three closely linked problems (ie, image tag assignment, refinement, and tag-based image retrieval) is presented While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, that is, estimating the relevance of a specific tag with respect to the visual content of a given image and its social context By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this article introduces a two-dimensional taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations For a head-to-head comparison with the state of the art, a new experimental protocol is presented, with training sets containing 10,000, 100,000, and 1 million images, and an evaluation on three test sets, contributed by various research groups Eleven representative works are implemented and evaluated Putting all this together, the survey aims to provide an overview of the past and foster progress for the near future

Optimized Graph Learning Using Partial Tags and Multiple Features for Image and Video Annotation

Abstract: In multimedia annotation, due to the time constraints and the tediousness of manual tagging, it is quite common to utilize both tagged and untagged data to improve the performance of supervised learning when only limited tagged training data are available. This is often done by adding a geometry-based regularization term in the objective function of a supervised learning model. In this case, a similarity graph is indispensable to exploit the geometrical relationships among the training data points, and the graph construction scheme essentially determines the performance of these graph-based learning algorithms. However, most of the existing works construct the graph empirically and are usually based on a single feature without using the label information. In this paper, we propose a semi-supervised annotation approach by learning an optimized graph (OGL) from multi-cues (i.e., partial tags and multiple features), which can more accurately embed the relationships among the data points. Since OGL is a transductive method and cannot deal with novel data points, we further extend our model to address the out-of-sample issue. Extensive experiments on image and video annotation show the consistent superiority of OGL over the state-of-the-art methods.

Circular Reranking for Visual Search

Abstract: Search reranking is regarded as a common way to boost retrieval precision. The problem nevertheless is not trivial especially when there are multiple features or modalities to be considered for search, which often happens in image and video retrieval. This paper proposes a new reranking algorithm, named circular reranking, that reinforces the mutual exchange of information across multiple modalities for improving search performance, following the philosophy that strong performing modality could learn from weaker ones, while weak modality does benefit from interacting with stronger ones. Technically, circular reranking conducts multiple runs of random walks through exchanging the ranking scores among different features in a cyclic manner. Unlike the existing techniques, the reranking procedure encourages interaction among modalities to seek a consensus that are useful for reranking. In this paper, we study several properties of circular reranking, including how and which order of information propagation should be configured to fully exploit the potential of modalities for reranking. Encouraging results are reported for both image and video retrieval on Microsoft Research Asia Multimedia image dataset and TREC Video Retrieval Evaluation 2007-2008 datasets, respectively.