A Comparison of Explicit and Implicit Graph Embedding Methods for Pattern Recognition

Conte, Donatello; Ramel, Jean-Yves; Sidère, Nicolas; Luqman, Muhammad; Gaüzère, Benoît; Gibert, Jaume; Brun, Luc; Vento, Mario

doi:10.1007/978-3-642-38221-5_9

Cited by 13 publications

(9 citation statements)

References 19 publications

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“…After showing the possible extension of the vocabulary, it is interesting to use graph embedding techniques (bridging the gap between structural and statistical symbol recognition) and graph kernels, which are basically targeted to reduce the time complexity (Conte et al, 2013;Foggia & Vento, 2010;Luqman, 2012;Riesen & Bunke, 2010).…”

Section: Discussionmentioning

confidence: 99%

BoR: BAG-OF-RELATIONS FOR SYMBOL RETRIEVAL

Santosh

Wendling

2014

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

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In this paper, we address a new scheme for symbol retrieval based on bag-of-relations (BoRs) which are computed between extracted visual primitives (e.g. circle and corner). Our features consist of pairwise spatial relations from all possible combinations of individual visual primitives. The key characteristic of the overall process is to use topological relation information indexed in BoRs and use this for recognition. As a consequence, directional relation matching takes place only with those candidates having similar topological configurations. A comprehensive study is made by using several different well-known datasets such as GREC, FRESH and SESYD, and includes a comparison with state-of-the-art descriptors. Experiments provide interesting results on symbol spotting and other user-friendly symbol retrieval applications.

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Section: Discussionmentioning

confidence: 99%

BoR: BAG-OF-RELATIONS FOR SYMBOL RETRIEVAL

Santosh

Wendling

2014

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

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“…Just to get an idea of the efficiency of these methods, we present in Table 1 an extract of the results of both methods, presented in Ref. 4, on known graph bases for classification.…”

Section: Graph Embeddingmentioning

confidence: 99%

Graphs in pattern recognition: successes, shortcomings, and perspectives

Conte

2023

J. Electron. Imag.

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Graphs in pattern recognition have been studied since the late seventies with alternating events of successes and failures. Far from wanting to propose a unique survey on the subject, we aim to present the areas in which graphs have been shown to be effective and to illustrate the problems still open in this research domain, with a focus on recent developments moving in the direction of deep learning. This work intends to arouse the interest of neophytes to this interesting domain that is the representation of the world through graphs.

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“…However k GA not only generalizes the dynamic time warping kernel k DTW , but also provides richer statistic measures by incorporating the whole spectrum of alignment costs {D P,Q (π),π ∈A(m, n)}. Intuitively, the global alignment kernel k GA allows one to define a new graph kernel, by measuring the warping alignment π between any types of graph characteristic sequences that have certain element orders with increasing structural variables, e.g, the graph embedding vectors proposed by Conte et al (2013), the depth-based complexity traces from expansion subgraphs of increasing sizes proposed by Bai and Hancock (2016) , or cycle characteristics with increasing lengths identified from the Ihara zeta function proposed by Ren et al (2011).…”

Section: Global Alignment Kernels From the Dynamic Time Warping Framementioning

confidence: 99%

Local-global nested graph kernels using nested complexity traces

Bai

Cui

Rossi

et al. 2020

Pattern Recognition Letters

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In this paper, we propose two novel local-global nested graph kernels, namely the nested aligned kernel and the nested reproducing kernel, drawing on depth-based complexity traces. Both of the nested kernels gauge the nested depth complexity trace through a family of K-layer expansion subgraphs rooted at the centroid vertex, i.e., the vertex with minimum shortest path length variance to the remaining vertices. Specifically, for a pair of graphs, we commence by computing the centroid depth-based complexity traces rooted at the centroid vertices. The first nested kernel is defined by measuring the global alignment kernel, which is based on the dynamic time warping framework, between the complexity traces. Since the required global alignment kernel incorporates the whole spectrum of alignment costs between the complexity traces, this nested kernel can provide rich statistic measures. The second nested kernel, on the other hand, is defined by measuring the basic reproducing kernel between the complexity traces. Since the associated reproducing kernel only requires time complexity O(1), this nested kernel has very low computational complexity. We theoretically show that both of the proposed nested kernels can simultaneously reflect the local and global graph characteristics in terms of the nested complexity traces. Experiments on standard graph datasets abstracted from bioinformatics and computer vision databases demonstrate the effectiveness and efficiency of the proposed graph kernels.

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A Comparison of Explicit and Implicit Graph Embedding Methods for Pattern Recognition

Cited by 13 publications

References 19 publications

BoR: BAG-OF-RELATIONS FOR SYMBOL RETRIEVAL

BoR: BAG-OF-RELATIONS FOR SYMBOL RETRIEVAL

Graphs in pattern recognition: successes, shortcomings, and perspectives

Local-global nested graph kernels using nested complexity traces

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