An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Matching by Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimax entropy principle and its application to texture modeling
Neural Computation
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Linear Programming Approach for the Weighted Graph Matching Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation Using Local Variation
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Transformed Component Analysis: Joint Estimation of Spatial Transformations and Image Components
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Boosting contextual information in content-based image retrieval
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
From frequent itemsets to semantically meaningful visual patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Context-Based Object-Class Recognition and Retrieval by Generalized Correlograms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining and cropping common objects from images
Proceedings of the international conference on Multimedia
Graph object oriented database for semantic image retrieval
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
Maximum likelihood for gaussians on graphs
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Common visual pattern discovery via graph matching
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Automatic learning of structural models of cartographic objects
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Efficient object-class recognition by boosting contextual information
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
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This paper presents the methodology and theory for automatic spatial pattern discovery from multiple attributed relational graph samples. The spatial pattern is modelled as a mixture of probabilistic parametric attributed relational graphs. A statistic learning procedure is designed to learn the parameters of the spatial pattern model from the attributed relational graph samples. The learning procedure is formulated as a combinatorial non-deterministic process, which uses the expectation-maximization (EM) algorithm to find the maximum-likelihood estimates for the parameters of the spatial pattern model. The learned model summarizes the samples and captures the statistic characteristics of the appearance and structure of the spatial pattern, which is observed under various conditions. It can be used to detect the spatial pattern in new samples. The proposed approach is applied to unsupervised visual pattern extraction from multiple images in the experiments.