Details
| Original language | English |
|---|---|
| Title of host publication | Advances in Information Retrieval |
| Subtitle of host publication | 42nd European Conference on IR Research, ECIR 2020, Proceedings |
| Editors | Joemon M. Jose, Emine Yilmaz, João Magalhães, Flávio Martins, Pablo Castells, Nicola Ferro, Mário J. Silva |
| Place of Publication | Cham |
| Pages | 289-296 |
| Number of pages | 8 |
| ISBN (electronic) | 978-3-030-45442-5 |
| Publication status | Published - 8 Apr 2020 |
| Event | 42nd European Conference on IR Research, ECIR 2020 - Lisbon, Portugal Duration: 14 Apr 2020 → 17 Apr 2020 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 12036 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
In the past few years, convolutional neural networks (CNNs) have achieved impressive results in computer vision tasks, which however mainly focus on photos with natural scene content. Besides, non-sensor derived images such as illustrations, data visualizations, figures, etc. are typically used to convey complex information or to explore large datasets. However, this kind of images has received little attention in computer vision. CNNs and similar techniques use large volumes of training data. Currently, many document analysis systems are trained in part on scene images due to the lack of large datasets of educational image data. In this paper, we address this issue and present SlideImages, a dataset for the task of classifying educational illustrations. SlideImages contains training data collected from various sources, e.g., Wikimedia Commons and the AI2D dataset, and test data collected from educational slides. We have reserved all the actual educational images as a test dataset in order to ensure that the approaches using this dataset generalize well to new educational images, and potentially other domains. Furthermore, we present a baseline system using a standard deep neural architecture and discuss dealing with the challenge of limited training data.
Keywords
- Classification dataset, Document figure classification, Educational documents
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Advances in Information Retrieval: 42nd European Conference on IR Research, ECIR 2020, Proceedings. ed. / Joemon M. Jose; Emine Yilmaz; João Magalhães; Flávio Martins; Pablo Castells; Nicola Ferro; Mário J. Silva. Cham, 2020. p. 289-296 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12036 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - SlideImages
T2 - 42nd European Conference on IR Research, ECIR 2020
AU - Morris, David
AU - Müller-Budack, Eric
AU - Ewerth, Ralph
N1 - Funding information: Acknowledgement. This work is financially supported by the German Federal Ministry of Education and Research (BMBF) and European Social Fund (ESF) (Inclu-siveOCW project, no. 01PE17004).
PY - 2020/4/8
Y1 - 2020/4/8
N2 - In the past few years, convolutional neural networks (CNNs) have achieved impressive results in computer vision tasks, which however mainly focus on photos with natural scene content. Besides, non-sensor derived images such as illustrations, data visualizations, figures, etc. are typically used to convey complex information or to explore large datasets. However, this kind of images has received little attention in computer vision. CNNs and similar techniques use large volumes of training data. Currently, many document analysis systems are trained in part on scene images due to the lack of large datasets of educational image data. In this paper, we address this issue and present SlideImages, a dataset for the task of classifying educational illustrations. SlideImages contains training data collected from various sources, e.g., Wikimedia Commons and the AI2D dataset, and test data collected from educational slides. We have reserved all the actual educational images as a test dataset in order to ensure that the approaches using this dataset generalize well to new educational images, and potentially other domains. Furthermore, we present a baseline system using a standard deep neural architecture and discuss dealing with the challenge of limited training data.
AB - In the past few years, convolutional neural networks (CNNs) have achieved impressive results in computer vision tasks, which however mainly focus on photos with natural scene content. Besides, non-sensor derived images such as illustrations, data visualizations, figures, etc. are typically used to convey complex information or to explore large datasets. However, this kind of images has received little attention in computer vision. CNNs and similar techniques use large volumes of training data. Currently, many document analysis systems are trained in part on scene images due to the lack of large datasets of educational image data. In this paper, we address this issue and present SlideImages, a dataset for the task of classifying educational illustrations. SlideImages contains training data collected from various sources, e.g., Wikimedia Commons and the AI2D dataset, and test data collected from educational slides. We have reserved all the actual educational images as a test dataset in order to ensure that the approaches using this dataset generalize well to new educational images, and potentially other domains. Furthermore, we present a baseline system using a standard deep neural architecture and discuss dealing with the challenge of limited training data.
KW - Classification dataset
KW - Document figure classification
KW - Educational documents
UR - http://www.scopus.com/inward/record.url?scp=85084183613&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-45442-5_36
DO - 10.1007/978-3-030-45442-5_36
M3 - Conference contribution
AN - SCOPUS:85084183613
SN - 9783030454418
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 289
EP - 296
BT - Advances in Information Retrieval
A2 - Jose, Joemon M.
A2 - Yilmaz, Emine
A2 - Magalhães, João
A2 - Martins, Flávio
A2 - Castells, Pablo
A2 - Ferro, Nicola
A2 - Silva, Mário J.
CY - Cham
Y2 - 14 April 2020 through 17 April 2020
ER -