A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Machine Learning
SVM selective sampling for ranking with application to data retrieval
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Graphical Models and Point Pattern Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid and brief communication: Active learning for image retrieval with Co-SVM
Pattern Recognition
Multimodal interactive transcription of text images
Pattern Recognition
Multiple-view multiple-learner active learning
Pattern Recognition
SALSAS: Sub-linear active learning strategy with approximate k-NN search
Pattern Recognition
Active learning with adaptive regularization
Pattern Recognition
Relaxation: Evaluation and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new graph matching method for point-set correspondence using the EM algorithm and Softassign
Computer Vision and Image Understanding
A cluster-assumption based batch mode active learning technique
Pattern Recognition Letters
Inconsistency-based active learning for support vector machines
Pattern Recognition
A Word-Based Naïve Bayes Classifier for Confidence Estimation in Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
Smooth point-set registration using neighboring constraints
Pattern Recognition Letters
Active learning of inverse models with intrinsically motivated goal exploration in robots
Robotics and Autonomous Systems
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We propose a method to perform active graph matching in which the active learner queries one of the nodes of the first graph and the oracle feedback is the corresponding node of the other graph. The method uses any graph matching algorithm that iteratively updates a probability matrix between nodes (Graduated Assignment, Expectation Maximisation or Probabilistic Relaxation). The oracle's feedback is used to update the costs between nodes and arcs of both graphs. We present and validate four different active strategies based on the probability matrix between nodes. It is not needed to modify the code of the graph-matching algorithms, since our method simply needs to read the probability matrix and to update the costs between nodes and arcs. Practical validation shows that with few oracle's feedbacks, the algorithm finds the labelling that the user considers optimal because imposing few labellings the other ones are corrected automatically.