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 -