Author: "Medina Pagola, José E." / Topic: approximate graph matching - Searchworks@Jio Institute Digital Library Search Results

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Start Over Author "Medina Pagola, José E." Topic approximate graph matching

Author: Acosta-Mendoza, Niusvel, Carrasco-Ochoa, Jesús Ariel, Martínez-Trinidad, José Fco., Gago-Alonso, Andrés, Medina-Pagola, José E., Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Martínez-Trinidad, José Francisco, editor, Carrasco-Ochoa, Jesús Ariel, editor, Olvera-López, José Arturo, editor, and Sarkar, Sudeep, editor
Published: 2018
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Author: Acosta-Mendoza, Niusvel, Gago-Alonso, Andrés, Carrasco-Ochoa, Jesús Ariel, Martínez-Trinidad, José Francisco, Medina-Pagola, José E., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ruiz-Shulcloper, José, editor, and Sanniti di Baja, Gabriella, editor
Published: 2013
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Author: Acosta-Mendoza, Niusvel, Gago-Alonso, Andrés, Medina-Pagola, José E., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Alvarez, Luis, editor, Mejail, Marta, editor, Gomez, Luis, editor, and Jacobo, Julio, editor
Published: 2012
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Author: Acosta-Mendoza, Niusvel, Gago-Alonso, Andrés, and Medina-Pagola, José E.
Subjects: IMAGE analysis, SUBGRAPHS, GRAPH theory, APPROXIMATION theory, DATA mining, COMPUTER algorithms, IMAGE representation, FEATURE selection
Abstract: Copyright of Revista Cubana de Ciencias Informáticas is the property of Universidad de las Ciencias Informaticas (UCI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Published: 2011

Author: Acosta-Mendoza, Niusvel, Gago-Alonso, Andrés, and Medina-Pagola, José E.
Subjects: *GRAPHIC methods, *COMPUTER algorithms, *IMAGE processing, *TOPOLOGY, *DATA mining, *APPROXIMATION theory, *IMAGE analysis, *IMAGING systems
Abstract: Abstract: The use of approximate graph matching for frequent subgraph mining has been identified in different applications as a need. To meet this need, several algorithms have been developed, but there are applications where it has not been used yet, for example image classification. In this paper, a new algorithm for mining frequent connected subgraphs over undirected and labeled graph collections VEAM (Vertex and Edge Approximate graph Miner) is presented. Slight variations of the data, keeping the topology of the graphs, are allowed in this algorithm. Approximate matching in existing algorithm (APGM) is only performed on vertex label set. In VEAM, the approximate matching between edge label set in frequent subgraph mining is included in the mining process. Also, a framework for graph-based image classification is introduced. The approximate method of VEAM was tested on an artificial image collection using a graph-based image representation proposed in this paper. The experimentation on this collection shows that our proposal gets better results than graph-based image classification using some algorithms reported in related work. [Copyright &y& Elsevier]
Published: 2012
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