An Efficiently Computable Metric for Comparing Polygonal Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symbol Recognition by Error-Tolerant Subgraph Matching between Region Adjacency Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Engineering Drawing Database Retrieval Using Statistical Pattern Spotting Techniques
GREC '99 Selected Papers from the Third International Workshop on Graphics Recognition, Recent Advances
Building Synthetic Graphical Documents for Performance Evaluation
Graphics Recognition. Recent Advances and New Opportunities
Pattern Recognition Methods for Querying and Browsing Technical Documentation
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Relational indexing of vectorial primitives for symbol spotting in line-drawing images
Pattern Recognition Letters
Symbol spotting in vectorized technical drawings through a lookup table of region strings
Pattern Analysis & Applications
A symbol spotting approach in graphical documents by hashing serialized graphs
Pattern Recognition
Bag-of-GraphPaths descriptors for symbol recognition and spotting in line drawings
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
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In this paper we propose an error tolerant subgraph matching algorithm based on bag-of-paths for solving the problem of symbol spotting in line drawings. Bag-of-paths is a factorized representation of graphs where the factorization is done by considering all the acyclic paths between each pair of connected nodes. Similar paths within the whole collection of documents are clustered and organized in a lookup table for efficient indexing. The lookup table contains the index key of each cluster and the corresponding list of locations as a single entry. The mean path of each of the clusters serves as the index key for each table entry. The spotting method is then formulated by a spatial voting scheme to the list of locations of the paths that are decided in terms of search of similar paths that compose the query symbol. Efficient indexing of common substructures helps to reduce the computational burden of usual graph based methods. The proposed method can also be seen as a way to serialize graphs which allows to reduce the complexity of the subgraph isomorphism. We have encoded the paths in terms of both attributed strings and turning functions, and presented a comparative results between them within the symbol spotting framework. Experimentations for matching different shape silhouettes are also reported and the method has been proved to work in noisy environment also.