An interactive method for the image alignment problem based on partially supervised correspondence

Cortés, Xavier; Serratosa, Francesc

doi:10.1016/j.eswa.2014.07.051

Cited by 25 publications

(11 citation statements)

References 49 publications

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“…Attributed graphs have been used in some pattern recognition¯elds such as object recognition, 16,27,39 scene view alignment, 3,20,30,43 multiple object alignment, 40,41 object characterization [7][8][9] interactive methods, 5 image registration, 31,32 tracking 34 among a great amount of other applications. Interesting reviews of techniques and applications are given in Refs.…”

Section: Introductionmentioning

confidence: 99%

Speeding up Fast Bipartite Graph Matching Through a New Cost Matrix

Serratosa

2015

Int. J. Patt. Recogn. Artif. Intell.

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Bipartite (BP) has been seen to be a fast and accurate suboptimal algorithm to solve the Error-Tolerant Graph Matching problem. Recently, Fast Bipartite (FBP) has been presented that obtains the same distance value and node labelings but in a reduced time. Both algorithms approximate the quadratic problem in a linear problem and they do it through a speci¯c cost matrix. FBP imposes the Edit costs to be de¯ned such as the Edit distance is a distance function. Originally, the Hungarian method was used but it has been seen the Jonker-Volgenant linear solver obtains similar results than the Hungarian method but with an important run time reduction. Nevertheless, this second solver has some convergence problems on some speci¯c cost matrices. The aim of this paper is to de¯ne a new cost matrix such that the Jonker-Volgenant solver converges and the matching algorithm obtains the same distance value than the BP algorithm.

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Section: Introductionmentioning

confidence: 99%

Speeding up Fast Bipartite Graph Matching Through a New Cost Matrix

Serratosa

2015

Int. J. Patt. Recogn. Artif. Intell.

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“…Similarly to [12], we approach the problem of learning the costs K v and K e for graph matching as a supervised learning problem [29]. The training set is composed of N observations.…”

Section: Learning K V and K E Costsmentioning

confidence: 99%

Learning graph-matching edit-costs based on the optimality of the oracle's node correspondences

Cortés

Serratosa

2015

Pattern Recognition Letters

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“…The goal of this interaction is to allow effective operation of the machine from the human end, while the machine simultaneously feeds back information that aids the human to make decisions. Human interaction has been recently applied to image alignment for robotics pose and image alignment estimation [33,34], 2D-camera calibration [35] or engineering drawing validation [36].…”

Section: Interactive Machine Learningmentioning

confidence: 99%

Interactive online learning for graph matching using active strategies

Conte

Serratosa

2020

Knowledge-Based Systems

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In some pattern recognition applications, objects are represented by attributed graphs, in which nodes represent local parts of the objects and edges represent relationships between these local parts. In this framework, the comparison between objects is performed through the distance between attributed graphs. Usually, this distance is a linear equation defined by some cost functions on the nodes and on the edges of both attributed graphs. In this paper, we present an online, active and interactive method for learning these cost functions, which works as follows. Graphs are provided to the learning algorithm by pairs in a sequential order (online). Then, a correspondence between them is computed,

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An interactive method for the image alignment problem based on partially supervised correspondence

Cited by 25 publications

References 49 publications

Speeding up Fast Bipartite Graph Matching Through a New Cost Matrix

Speeding up Fast Bipartite Graph Matching Through a New Cost Matrix

Learning graph-matching edit-costs based on the optimality of the oracle's node correspondences

Interactive online learning for graph matching using active strategies

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