Efficient algorithms for finding maximum matching in graphs

Galil, Zvi

doi:10.1145/6462.6502

Cited by 408 publications

(206 citation statements)

References 37 publications

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“…The simplest way to approach optimality is to look for a maximum weighted matching in this bipartite graph [9]. After removing "weak" edges that fall below a user specified threshold, we search the graph for a subset of the edges that Table 3.…”

Section: Projective Transformationsmentioning

confidence: 99%

Autoplex: Automated Discovery of Content for Virtual Databases

Berlin

Motro

2001

Cooperative Information Systems

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Abstract. Most virtual database systems are suitable for environments in which the set of member information sources is small and stable. Consequently, present virtual database systems do not scale up very well. The main reason is the complexity and cost of incorporating new information sources into the virtual database. In this paper we describe a system, called Autoplex, which uses machine learning techniques for automating the discovery of new content for virtual database systems. Autoplex assumes that several information sources have already been incorporated ("mapped") into the virtual database system by human experts (as done in standard virtual database systems). Autoplex learns the features of these examples. It then applies this knowledge to new candidate sources, trying to infer views that "resemble" the examples. In this paper we report initial results from the Autoplex project.

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Section: Projective Transformationsmentioning

confidence: 99%

Autoplex: Automated Discovery of Content for Virtual Databases

Berlin

Motro

2001

Cooperative Information Systems

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“…The structural matching is treated as Maximum Weight Bipartite Matching problem that can be solved in polynomial time using, for example, Kuhn's Hungarian method [18]. We use this method for two purposes: (i) to calculate semantic similarity between concept descriptions, (ii) to compute similarity of complex WSDL concepts taking into account their constituents (sub-types).…”

Section: The Wsdl Matching Methods (Wsdl-m2)mentioning

confidence: 99%

Leveraging Web Services Discovery with Customizable Hybrid Matching

Kokash

Heuvel

D’Andrea

2006

Service-Oriented Computing – ICSOC 2007

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Abstract. Improving web service discovery constitutes a vital step for making the Service Oriented Computing (SOC) vision of dynamic service selection, composition and deployment, a reality. Matching allows for comparing service requests of users with descriptions of available service implementations, and sits at the heart of the service discovery process. During the last years, several matching algorithms for comparing user requests to service interfaces have been suggested, but unfortunately there are no consistent comparative experimental evaluations of existing service discovery methods. This paper firstly reviews several state-ofthe-art approaches to matching using syntactic, semantic and structural information from service interface descriptions. Secondly, it evaluates the efficacy of several key similarity metrics that underpin these approaches, using a uniform corpus of web services. Thirdly, this paper develops and experiments with a novel style of matching that allows for blending various existing matching approaches and makes them configurable to cater service discovery given domain-specific constraints and requirements.

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“…This situation can be represented by a weighted bipartite graph, G = (V, E) [29]. A graph is bipartite if there exists a partition of the vertex set V = V 1 ∪V 2 so that both V 1 and V 2 are independent sets, and an edge, e v 1 v 2 ∈ E, can only link v 1 ∈ V 1 to v 2 ∈ V 2 .…”

Section: Pedestrian Trackingmentioning

confidence: 99%

Robust pedestrian detection and tracking in crowded scenes

Kelly

O’Connor

Smeaton

2009

Image and Vision Computing

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In this paper, a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes is presented. Pedestrian detection is performed via a 3D clustering process within a region-growing framework. The clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. Pedestrian tracking is achieved by formulating the track matching process as a weighted bipartite graph and using a Weighted Maximum Cardinality Matching scheme. The approach is evaluated using both indoor and outdoor sequences, captured using a variety of different camera placements and orientations, that feature significant challenges in terms of the number of pedestrians present, their interactions and scene lighting conditions. The evaluation is performed against a manually generated groundtruth for all sequences. Results point to the extremely accurate performance of the proposed approach in all cases.

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Efficient algorithms for finding maximum matching in graphs

Cited by 408 publications

References 37 publications

Autoplex: Automated Discovery of Content for Virtual Databases

Autoplex: Automated Discovery of Content for Virtual Databases

Leveraging Web Services Discovery with Customizable Hybrid Matching

Robust pedestrian detection and tracking in crowded scenes

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