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Auflistung nach Autor:in "Pryakhin, Alexey"

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  • Konferenzbeitrag
    Effective and Efficient Indexing for Large Video Databases
    (Datenbanksysteme in Business, Technologie und Web (BTW 2007) – 12. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 2007) Böhm, Christian; Kunath, Peter; Pryakhin, Alexey; Schubert, Matthias
    Content based multimedia retrieval is an important topic in database systems. An emerging and challenging topic in this area is the content based search in video data. A video clip can be considered as a sequence of images or frames. Since this representation is too complex to facilitate efficient video retrieval, a video clip is often summarized by a more concise feature representation. In this paper, we transform a video clip into a set of probabilistic feature vectors (pfvs). In our case, a pfv corresponds to a Gaussian in the feature space of frames. We demonstrate that this representation is well suited for accurate video retrieval. The use of pfvs allows us to calculate confidence values for frames or sets of frames for being contained within a given video in the database. These confidence values can be employed to specify two types of queries. The first type of query retrieves the videos stored in the database which contain a given set of frames with a probability that is larger than a given thresh-old value. Furthermore, we introduce a probabilistic ranking query retrieving the k database videos which contain the given query set with the highest probabilities. To efficiently process these queries, we introduce query algorithms on set-valued objects. Our solution is based on the Gauss-tree, an index structure for efficiently managing Gaussians in arbitrary vector spaces. Our experimental evaluation demonstrates that sets of probabilistic feature vectors yield a compact and descriptive representation of video clips. Additionally, we show that our new query algorithms outperform competitive approaches when answering the given types of queries on a database of over 900 real world video clips.
  • Konferenzbeitrag
    Efficient Reverse k-Nearest Neighbor Estimation
    (Datenbanksysteme in Business, Technologie und Web (BTW 2007) – 12. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 2007) Achtert, Elke; Böhm, Christian; Kröger, Peer; Kunath, Peter; Pryakhin, Alexey; Renz, Matthias
    The reverse k-nearest neighbor (RkNN) problem, i.e. finding all objects in a data set the k-nearest neighbors of which include a specified query object, has received increasing attention recently. Many industrial and scientific applications call for solutions of the RkNN problem in arbitrary metric spaces where the data objects are not Euclidean and only a metric distance function is given for specifying object similarity. Usually, these applications need a solution for the generalized problem where the value of k is not known in advance and may change from query to query. In addition, many applications require a fast approximate answer of RkNN-queries. For these scenarios, it is important to generate a fast answer with high recall. In this paper, we propose the first approach for efficient approximative RkNN search in arbitrary metric spaces where the value of k is specified at query time. Our approach uses the advantages of existing metric index structures but proposes to use an approximation of the nearest-neighbor-distances in order to prune the search space. We show that our method scales significantly better than existing non-approximative approaches while producing an approximation of the true query result with a high recall.