Visual content-based web page categorization with deep transfer learning and metric learning.
[EN]The growing amounts of online multimedia content challenge the current search, recommendation and information retrieval systems. Information in the form of visual elements is highly valuable in a range of web mining tasks. However, the mining of these resources is a difficult task due to the com...
| Autores: | , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universidad de Salamanca (USAL) |
| Repositorio: | GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| OAI Identifier: | oai:gredos.usal.es:10366/157119 |
| Acceso en línea: | http://hdl.handle.net/10366/157119 |
| Access Level: | acceso abierto |
| Palabra clave: | Web page categorization Metric learning Transfer learning Deep learning 1203.17 Informática |
| id |
ES_20162ef2ba766f4db907a9d82d5ed2b4 |
|---|---|
| oai_identifier_str |
oai:gredos.usal.es:10366/157119 |
| network_acronym_str |
ES |
| network_name_str |
España |
| repository_id_str |
|
| spelling |
Visual content-based web page categorization with deep transfer learning and metric learning.López Sánchez, DanielGonzález Arrieta, María AngélicaCorchado Rodríguez, Juan ManuelWeb page categorizationMetric learningTransfer learningDeep learning1203.17 Informática[EN]The growing amounts of online multimedia content challenge the current search, recommendation and information retrieval systems. Information in the form of visual elements is highly valuable in a range of web mining tasks. However, the mining of these resources is a difficult task due to the complexity and variability of images, and the cost of collecting big enough datasets to successfully train accurate deep learning models. This paper proposes a novel framework for the categorization of web pages on the basis of their visual content. This is achieved by exploring the joint application of a transfer learning strategy and metric learning techniques to build a Deep Convolutional Neural Network (DCNN) for feature extrac- tion, even when training data is scarce. The obtained experimental results evidence that the proposed approach outperforms the state-of-the-art handcrafted image descriptors and achieves a high categoriza- tion accuracy. In addition, we address the problem of over-time learning, so the proposed framework can learn to identify new web page categories as new labeled images are provided at test time. As a result, prior knowledge of the complete set of possible web categories is not necessary in the initial training phase.Elsevier202420242019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10366/157119reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)InglésAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1571192026-06-07T06:28:51Z |
| dc.title.none.fl_str_mv |
Visual content-based web page categorization with deep transfer learning and metric learning. |
| title |
Visual content-based web page categorization with deep transfer learning and metric learning. |
| spellingShingle |
Visual content-based web page categorization with deep transfer learning and metric learning. López Sánchez, Daniel Web page categorization Metric learning Transfer learning Deep learning 1203.17 Informática |
| title_short |
Visual content-based web page categorization with deep transfer learning and metric learning. |
| title_full |
Visual content-based web page categorization with deep transfer learning and metric learning. |
| title_fullStr |
Visual content-based web page categorization with deep transfer learning and metric learning. |
| title_full_unstemmed |
Visual content-based web page categorization with deep transfer learning and metric learning. |
| title_sort |
Visual content-based web page categorization with deep transfer learning and metric learning. |
| dc.creator.none.fl_str_mv |
López Sánchez, Daniel González Arrieta, María Angélica Corchado Rodríguez, Juan Manuel |
| author |
López Sánchez, Daniel |
| author_facet |
López Sánchez, Daniel González Arrieta, María Angélica Corchado Rodríguez, Juan Manuel |
| author_role |
author |
| author2 |
González Arrieta, María Angélica Corchado Rodríguez, Juan Manuel |
| author2_role |
author author |
| dc.subject.none.fl_str_mv |
Web page categorization Metric learning Transfer learning Deep learning 1203.17 Informática |
| topic |
Web page categorization Metric learning Transfer learning Deep learning 1203.17 Informática |
| description |
[EN]The growing amounts of online multimedia content challenge the current search, recommendation and information retrieval systems. Information in the form of visual elements is highly valuable in a range of web mining tasks. However, the mining of these resources is a difficult task due to the complexity and variability of images, and the cost of collecting big enough datasets to successfully train accurate deep learning models. This paper proposes a novel framework for the categorization of web pages on the basis of their visual content. This is achieved by exploring the joint application of a transfer learning strategy and metric learning techniques to build a Deep Convolutional Neural Network (DCNN) for feature extrac- tion, even when training data is scarce. The obtained experimental results evidence that the proposed approach outperforms the state-of-the-art handcrafted image descriptors and achieves a high categoriza- tion accuracy. In addition, we address the problem of over-time learning, so the proposed framework can learn to identify new web page categories as new labeled images are provided at test time. As a result, prior knowledge of the complete set of possible web categories is not necessary in the initial training phase. |
| publishDate |
2019 |
| dc.date.none.fl_str_mv |
2019 2024 2024 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
http://hdl.handle.net/10366/157119 |
| url |
http://hdl.handle.net/10366/157119 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.rights.none.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| eu_rights_str_mv |
openAccess |
| dc.publisher.none.fl_str_mv |
Elsevier |
| publisher.none.fl_str_mv |
Elsevier |
| dc.source.none.fl_str_mv |
reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca instname:Universidad de Salamanca (USAL) |
| instname_str |
Universidad de Salamanca (USAL) |
| reponame_str |
GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| collection |
GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| repository.name.fl_str_mv |
|
| repository.mail.fl_str_mv |
|
| _version_ |
1869404438602448896 |
| score |
15,301603 |