gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
STRG-Index: spatio-temporal region graph indexing for large video databases
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
From frequent itemsets to semantically meaningful visual patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
Video mining with frequent itemset configurations
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Mining spatiotemporal patterns in dynamic plane graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
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Given a graph, frequent graph mining extracts subgraphs appearing frequently as useful knowledge. This paper proposes to exploit graph mining that discovers knowledge without supervision to realize unsupervised image analysis. In particular, we present a background subtraction algorithm from videos in which the background model is acquired without supervision. The targets of our algorithm are videos in which a moving object passes in front of a surveillance camera. After transforming each video frame into a region adjacency graph, our method discovers the subgraph representing the background, exploiting the fact that the background appears in more frames than the moving object.