Efficient Graph Matching for Video Indexing

Shearer, Kim; Bunke, Horst; Venkatesh, Svetha; Kieronska, D.

doi:10.1007/978-3-7091-6487-7_6

Cited by 13 publications

(7 citation statements)

References 14 publications

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“…Bunke and al. [4] have proposed, in the field of graph matching, to split the matching into 3 variants named type-0, type-1, type-2 with a increasing strictness of the matching.…”

Section: The Type-n Graphmentioning

confidence: 99%

“…We have on the left the set of possible type-2 relations as in [4]. On the right, we have two drawings with their associated graphs.…”

Section: Fig 1 Orientation and Type-2mentioning

confidence: 99%

“…In GBR'99 [8], Bunke and al. in [4] presents some works about type-n graphs, and Deruyver and al. in [5] about semantic graphs.…”

Section: Introductionmentioning

confidence: 99%

“…We have many discussions about the possible extensions of the RAGs. [4] and [5] present different kind of graphs with many different types of edges.…”

Section: Introductionmentioning

confidence: 99%

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Pyramid and Interior

Pailloncy

2000

Advances in Pattern Recognition

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A Pyramid: a hierarchy of Region Adjacency Graphs (RAG) has only one type of edge "to be neighbor with". We want to add some new types of edge. The relation "to be inside" is already present as a special case of the neighborhood relation. We detail the difference between a neighborhood relation and an interior relation. Then we show that some new types of interior relations are compatible with the process of building a pyramid.

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“…Bunke and al. [4] have proposed, in the field of graph matching, to split the matching into 3 variants named type-0, type-1, type-2 with a increasing strictness of the matching.…”

Section: The Type-n Graphmentioning

confidence: 99%

“…We have on the left the set of possible type-2 relations as in [4]. On the right, we have two drawings with their associated graphs.…”

Section: Fig 1 Orientation and Type-2mentioning

confidence: 99%

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Pyramid and Interior

Pailloncy

2000

Advances in Pattern Recognition

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“…There are several applications, for example, in pattern recognition [12,13,22,28,29,35,37], machine learning [21], computer vision [14], image and video analysis [9,11,24,26,38] and information retrieval [19], where there is the need to measure the similarity between objects. If graphs are used for the representation of structured objects, then matching and comparing objects becomes equivalent to determining the similarity between graphs ( [1]).…”

Section: Introductionmentioning

confidence: 99%

Challenging Complexity of Maximum Common Subgraph Detection Algorithms: A Performance Analysis of Three Algorithms on a Wide Database of Graphs

Conte¹,

Foggia²,

Vento³

2007

JGAA

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Graphs are an extremely general and powerful data structure. In pattern recognition and computer vision, graphs are used to represent patterns to be recognized or classified. Detection of maximum common subgraph (MCS) is useful for matching, comparing and evaluate the similarity of patterns. MCS is a well known NP-complete problem for which optimal and suboptimal algorithms are known from the literature. Nevertheless, until now no effort has been done for characterizing their performance. The lack of a large database of graphs makes the task of comparing the performance of different graph matching algorithms difficult, and often the selection of an algorithm is made on the basis of a few experimental results available. In this paper, three optimal and well-known algorithms for maximum common subgraph detection are described. Moreover a large database containing various categories of pairs of graphs (e.g. random graphs, meshes, bounded valence graphs), is presented, and the performance of the three algorithms is evaluated on this database.

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