Matching oversegmented 3D images to models using association graphs
Image and Vision Computing
Stereo Correspondence Through Feature Grouping and Maximal Cliques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching feature points in image sequences through a region-based method
Computer Vision and Image Understanding
A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Efficient Subgraph Isomorphism Detection: A Decomposition Approach
IEEE Transactions on Knowledge and Data Engineering
A Comparison of Algorithms for Maximum Common Subgraph on Randomly Connected Graphs
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Scalable subgraph mapping for acyclic computation accelerators
CASES '06 Proceedings of the 2006 international conference on Compilers, architecture and synthesis for embedded systems
Exploiting Narrow Accelerators with Data-Centric Subgraph Mapping
Proceedings of the International Symposium on Code Generation and Optimization
Temporal Analysis of Mammograms Based on Graph Matching
IWDM '08 Proceedings of the 9th international workshop on Digital Mammography
BinHunt: Automatically Finding Semantic Differences in Binary Programs
ICICS '08 Proceedings of the 10th International Conference on Information and Communications Security
Comparative sequence and structural analyses of neuroserpin: the serine protease inhibitor family
ISB '10 Proceedings of the International Symposium on Biocomputing
Connected substructure similarity search
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
IEEE Transactions on Information Technology in Biomedicine
High efficiency and quality: large graphs matching
Proceedings of the 20th ACM international conference on Information and knowledge management
A new approach and faster exact methods for the maximum common subgraph problem
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
Improved detection of cancer in screening mammograms by temporal comparison
IWDM'10 Proceedings of the 10th international conference on Digital Mammography
Detection of protein assemblies in crystals
CompLife'05 Proceedings of the First international conference on Computational Life Sciences
Finding top-k similar graphs in graph databases
Proceedings of the 15th International Conference on Extending Database Technology
Efficient subgraph similarity all-matching
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Multi-robot olfactory search in structured environments
Robotics and Autonomous Systems
High efficiency and quality: large graphs matching
The VLDB Journal — The International Journal on Very Large Data Bases
Systematic audit of third-party android phones
Proceedings of the 4th ACM conference on Data and application security and privacy
Hi-index | 0.00 |
Graph theory offers a convenient and highly attractive approach to various tasks of pattern recognition. Provided there is a graph representation of the object in question (e.g. a chemical structure or protein fold), the recognition procedure is reduced to the problem of common subgraph isomorphism (CSI). Complexity of this problem shows combinatorial dependence on the size of input graphs, which in many practical cases makes the approach computationally intractable. Among the optimal algorithms for CSI, the leading place in practice belongs to algorithms based on maximal clique detection in the association graph. Backtracking algorithms for CSI, first developed two decades ago, are rarely used. We propose an improved backtracking algorithm for CSI, which differs from its predecessors by better search strategy and is therefore more efficient. We found that the new algorithm outperforms the traditional maximal clique approach by orders of magnitude in computational time.