A Randomized Heuristic for Scene Recognition by Graph Matching

Bóeres, Maria Claudia Silva; Ribeiro, Celso C.; Bloch, Isabelle

doi:10.1007/978-3-540-24838-5_8

Cited by 36 publications

(34 citation statements)

References 16 publications

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“…Although the running time of GSimJoin grows when we move towards larger 's, it is still always faster than AppFull on AIDS. For threshold settings in [1,3] on PROTEIN, GSimJoin is also more timeefficient. AppFull spends less time on PROTEIN when is 4, however, GSimJoin can output all the answers while AppFull generates a set of candidates that still need verification.…”

Section: F Comparing With Appfullmentioning

confidence: 99%

Section: A Minimum Graph Edit Operationsmentioning

confidence: 99%

“…The time complexity is reduced to ( (| | + | |) log | |). 3 The greedy algorithm for set cover problem chooses the set which contains the largest number of uncovered elements at each stage. Algorithm 2 presents the approximate algorithm and it is guaranteed to return a lower bound of the exact answer.…”

Section: A Minimum Graph Edit Operationsmentioning

confidence: 99%

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Efficient Graph Similarity Joins with Edit Distance Constraints

Zhao

Xiao²,

Lin

et al. 2012

2012 IEEE 28th International Conference on Data Engineering

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Abstract-Graphs are widely used to model complicated data semantics in many applications in bioinformatics, chemistry, social networks, pattern recognition, etc. A recent trend is to tolerate noise arising from various sources, such as erroneous data entry, and find similarity matches. In this paper, we study the graph similarity join problem that returns pairs of graphs such that their edit distances are no larger than a threshold. Inspired by the -gram idea for string similarity problem, our solution extracts paths from graphs as features for indexing. We establish a lower bound of common features to generate candidates. An efficient algorithm is proposed to exploit both matching and mismatching features to improve the filtering and verification on candidates. We demonstrate the proposed algorithm significantly outperforms existing approaches with extensive experiments on publicly available datasets.

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Section: F Comparing With Appfullmentioning

confidence: 99%

Section: A Minimum Graph Edit Operationsmentioning

confidence: 99%

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Efficient Graph Similarity Joins with Edit Distance Constraints

Zhao

Xiao²,

Lin

et al. 2012

2012 IEEE 28th International Conference on Data Engineering

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show abstract

“…To compare medical images of a brain model brain [2], used graphs. Since the medical image is often noisy and oversegmented, the comparison of graphs in this context is based on a matching one-to-many (one region of the image model can correspond to several regions of the medical image).…”

Section: Similarity Measuresmentioning

confidence: 99%

“…Let CH G ={chm 1 ,chm 2 , …,chm n } the set of paths of G and CH G' ={chm' 1 ,chm' 2 , …,chm' n' } the set of paths of graphs. n (resp.…”

Section: Fig 3: Overall Architecture Of the Clustering Processmentioning

confidence: 99%

Structural Clustering Multimedia Documents: An Approach based on Semantic Sub-graph Isomorphism

Idarrou¹,

Mammass²

2012

IJCA

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The works that used graphs to represent documents has referred to the richness of these expressive tools. However, the exploited graph theory could be of great interest concerning the evaluation of similarity between these documents, both in documentary classification and the information retrieval. In structural classification of the documents, object of this work, the similarity measure is a crucial step. In many applications, this step results in a subgraph isomorphism problem. This problem is known in graph theory by a combinatorial explosion. To get around this problem, we propose to consider a graph as a set of paths that compose it. The matching, paths allows reducing the combinatorial cost.We propose a structural measure based on the sub-graph isomorphism and we discuss the quality of our classifier, especially the separation of classes. We'd like to show that our measure is structural, not a "surface measure" and evaluate our approach on a corpus of multimedia documents extracted, randomly, from the INEX 2007 corpus.

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Graph Edit Distance – Optimal and Suboptimal Algorithms with Applications

Bunke

Riesen

2009

Analysis of Complex Networks

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A Randomized Heuristic for Scene Recognition by Graph Matching

Cited by 36 publications

References 16 publications

Efficient Graph Similarity Joins with Edit Distance Constraints

Efficient Graph Similarity Joins with Edit Distance Constraints

Structural Clustering Multimedia Documents: An Approach based on Semantic Sub-graph Isomorphism

Graph Edit Distance – Optimal and Suboptimal Algorithms with Applications

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