How and Why Pattern Recognition and Computer Vision Applications Use Graphs

Conte, Donatello; Foggia, Pasquale; Sansone, Carlo; Vento, Mario

doi:10.1007/978-3-540-68020-8_4

Cited by 30 publications

(13 citation statements)

References 139 publications

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“…We use the definition of ϕ that is similar to Eshera and Fu's, given in Eq. (1). While this makes the following analysis specific to one particular algorithm, this algorithm is typical of many.…”

Section: Standard Tree Searchmentioning

confidence: 99%

“…Graph matching has been used in many applications, particularly in the field of visual media processing. They include 2D and 3D image analysis (e.g., shape recognition), interactive image editing (e.g., patch-based image synthesis), document processing (e.g., handwritten character recognition), biometric identification (e.g., human face recognition), image databases (e.g., image retrieval), video analysis (e.g., object tracking), and biomedical applications (e.g., identification of coronary arteries from medical images) [1][2][3][4]. A typical problem in shape recognition can be tackled in three steps, namely, the decomposition of a shape into parts, the representation of the parts and their relations using a graph, and the search for correspondence between this graph and that of a known object.…”

Section: Introductionmentioning

confidence: 99%

“…Interested readers are referred to relevant review papers, e.g., Refs. [1,3], for other problems in visual media processing which can be posed as graph matching problems.…”

Section: Introductionmentioning

confidence: 99%

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Inexact graph matching using a hierarchy of matching processes

Morrison

Zou

2015

Comp. Visual Media

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Inexact graph matching algorithms have proved to be useful in many applications, such as character recognition, shape analysis, and image analysis. Inexact graph matching is, however, inherently an NP-hard problem with exponential computational complexity. Much of the previous research has focused on solving this problem using heuristics or estimations. Unfortunately, many of these techniques do not guarantee that an optimal solution will be found. It is the aim of the proposed algorithm to reduce the complexity of the inexact graph matching process, while still producing an optimal solution for a known application. This is achieved by greatly simplifying each individual matching process, and compensating for lost robustness by producing a hierarchy of matching processes. The creation of each matching process in the hierarchy is driven by an application-specific criterion that operates at the subgraph scale. To our knowledge, this problem has never before been approached in this manner. Results show that the proposed algorithm is faster than two existing methods based on graph edit operations. The proposed algorithm produces accurate results in terms of matching graphs, and shows promise for the application of shape matching. The proposed algorithm can easily be extended to produce a sub-optimal solution if required.

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Section: Standard Tree Searchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Inexact graph matching using a hierarchy of matching processes

Morrison

Zou

2015

Comp. Visual Media

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“…Graph-based methodologies have been used as powerful tools for pattern recognition and computer vision since the late 1970s [1]. They are commonly related to a problem called graph matching, which includes graph isomorphism, sub-graph isomorphism, and maximum common sub-graph.…”

Section: Introductionmentioning

confidence: 99%

Tree Search Based Determination of Graph Isomorphism

Kim

Baek²,

Lee

2014

2014 8th International Conference on Future Generation Communication and Networking

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Graph isomorphism (GI) is a fundamental problem that commonly appears in graph-based applications, such as pattern recognition and computer vision. This paper presents a tree search based method for the determination of GI. In the proposed method, all possible sequences of a source graph are represented as all paths on a tree, and compared with an arbitrary sequence of a destination graph by an evaluation function in terms of bijectivity. Experimental results show that the proposed method is more efficient than an existing solution especially for that given two graphs are isomorphic.

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“…It can be used in pattern recognition [1,6], artificial visual sense [4,8], and molecule isomorphism judgment [2,11]. Because these problems are of great importance in scientific research and practical application, there are numerous scientists who consider the graph isomorphism issue to be their primary research area [9].…”

Section: Introductionmentioning

confidence: 99%

The SVE Method for Regular Graph Isomorphism Identification

Shang

Feng

Chen

et al. 2015

Circuits Syst Signal Process

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A regular graph is a type of graph that has particular features. Such graphs have great symmetry, which increases the difficulty of isomorphism identification. In this paper, we propose a new method to address regular graph isomorphism identification. We make some adjustments to the original circuit simulation (CS) method in the graph isomorphism identification process, using single-vertex excitation (SVE) in place of the original excitation, which could remove the confusion of corresponding vertices caused by the graph's symmetry. This new SVE method could widen the range of the application of the original CS method and create better performance in time consumption compared with other traditional methods. The performance of the SVE method is provided in this paper illuminating its practicality and superiority.

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How and Why Pattern Recognition and Computer Vision Applications Use Graphs

Cited by 30 publications

References 139 publications

Inexact graph matching using a hierarchy of matching processes

Inexact graph matching using a hierarchy of matching processes

Tree Search Based Determination of Graph Isomorphism

The SVE Method for Regular Graph Isomorphism Identification

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