Graph database filtering using decision trees

Image Analysis and Processing – ICIAP 2005

Neuhaus

2005

Abstract. Structural pattern representations, especially graphs, have advantages over feature vectors. However, they also suffer from a number of disadvantages, for example, their high computational complexity. Moreover, we observe that in the field of statistical pattern recognition a number of powerful concepts emerged recently that have no equivalent counterpart in the domain of structural pattern recognition yet. Examples include multiple classifier systems and kernel methods. In this paper, we survey a number of recent developments that may be suitable to overcome some of the current limitations of graph based representations in pattern recognition.

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Section: Fast Retrieval Of Graphs From Large Databasesmentioning

confidence: 99%

Section: Fast Retrieval Of Graphs From Large Databasesmentioning

confidence: 99%

Section: Fast Retrieval Of Graphs From Large Databasesmentioning

confidence: 99%

See 1 more Smart Citation

Graph Matching – Challenges and Potential Solutions

Image Analysis and Processing – ICIAP 2005

Neuhaus

2005

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“…In contrast with the work reported in [11], which was restricted to the case of graph isomorphism, the present paper deals with the problem of subgraph-isomorphism. Database filtering in conjunction with subgraph-isomorphism search was also studied in [13]. However, the decision trees used in [13] were identical to the decision trees used in [11,12] for graph-isomorphism, and the case of subgraph-isomorphism was dealt with by means of an extended decision tree traversal procedure.…”

Section: Introductionmentioning

confidence: 99%

“…Database filtering in conjunction with subgraph-isomorphism search was also studied in [13]. However, the decision trees used in [13] were identical to the decision trees used in [11,12] for graph-isomorphism, and the case of subgraph-isomorphism was dealt with by means of an extended decision tree traversal procedure. By contrast, a generalized decision tree induction procedure is proposed in the present paper.…”

Section: Introductionmentioning

confidence: 99%

Decision Tree Structures for Graph Database Filtering

Lecture Notes in Computer Science

2004

Abstract. In structural pattern recognition it is often required to match an unknown sample against a database of candidate patterns in order to find the most similar prototype. If the patterns are represented using graphs, the sample's graph is matched against a database of model graphs and the pattern recognition problem is turned into a graph matching problem. Graph matching is a powerful yet computationally expensive procedure. If the unknown sample is matched against a whole database of prototypes, the size of the database is introduced as an additional factor into the overall complexity of the matching process. To reduce the influence of that factor an approach based on machine learning techniques is proposed in this paper. The graphs are represented using feature vectors. Based on these vectors a decision tree is built to index the database. The decision tree allows at runtime to eliminate a number of graphs from the database as possible matching candidates. Experimental results are reported demonstrating the efficiency of the proposed filtering procedure. The work presented in this paper extends previous studies from the case of graph-isomorphism to the problem of subgraph-isomorphism.

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Decision Trees for Error-Tolerant Graph Database Filtering

Graph-Based Representations in Pattern Recognition

2005