Affiliations: [a] Université de Lyon, CNRS, INSA-Lyon, LIRIS, France | [b] Université de Lyon, Université de St-Etienne, UMR CNRS 5516, Laboratoire Hubert-Curien, France | [c] Alcatel-Lucent Bell Labs, Centre de Villarceaux, Nozay, France | [x] IRPI-CNR, Torino, Italy | [y] University of Torino, Torino, Italy | [z] University of Paris-Nord, Paris, France
Correspondence:
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Corresponding author: Adriana Prado, Université de Lyon, CNRS, INSA-Lyon, LIRIS, UMR 5205, F-69621, France. E-mail: [email protected].
Abstract: Dynamic graph mining is the task of searching for subgraph patterns that capture the evolution of a dynamic graph. In this paper, we are interested in mining dynamic graphs in videos. A video can be regarded as a dynamic graph, whose evolution over time is represented by a series of plane graphs, one graph for each video frame. As such, subgraph patterns in this series may correspond to objects that frequently appear in the video. Furthermore, by associating spatial information to each of the nodes in these graphs, it becomes possible to track a given object through the video in question. We present, in this paper, two plane graph mining algorithms, called plagram and dyplagram, for the extraction of spatiotemporal patterns. A spatiotemporal pattern is a set of occurrences of a given subgraph pattern which are not too far apart w.r.t time nor space. Experiments demonstrate that our algorithms are effective even in contexts where general-purpose algorithms would not provide the complete set of frequent subgraphs. We also show that they give promising results when applied to object tracking in videos.
Keywords: Dynamic graphs, graph mining, video applications, plane graphs
