Figures in Scientific Open Access Publications

Sohmen, Lucia; Charbonnier, Jean; Blümel, Ina; Wartena, Christian; Heller, Lambert

doi:10.1007/978-3-030-00066-0_19

Cited by 7 publications

(7 citation statements)

References 8 publications

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“…One of the goals of our project is to find good keywords for images from scientific publications. These images often have very long captions (see Sohmen et al, 2018). Thus, the captions and eventually the sentences explicitly referring to an image usually will provide enough text for extracting words describing the image (Josi et al, 2018).…”

Section: Discussion and Future Workmentioning

confidence: 99%

Predicting Word Concreteness and Imagery

Charbonnier¹,

Wartena²

2019

Proceedings of the 13th International Conference on Computational Semantics - Long Papers

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Concreteness of words has been studied extensively in psycholinguistic literature. A number of datasets has been created with average values for perceived concreteness of words. We show that we can train a regression model on these data, using word embeddings and morphological features. We evaluate the model on 7 publicly available datasets and show that concreteness and imagery values can be predicted with high accuracy. Furthermore, we analyse typical contexts of abstract and concrete words and review the potentials of concreteness prediction for image annotation.

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Section: Discussion and Future Workmentioning

confidence: 99%

Predicting Word Concreteness and Imagery

Charbonnier¹,

Wartena²

2019

Proceedings of the 13th International Conference on Computational Semantics - Long Papers

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“…While document scans and born-digital educational illustrations have materially different appearance, these papers show that the utility of deep neural networks is not limited to scene image tasks. A classification dataset of scientific illustrations was created for the NOA project [14]. However, their dataset is not publicly available, and does not draw as many distinctions between types of educational illustrations.…”

Section: Related Workmentioning

confidence: 99%

SlideImages: A Dataset for Educational Image Classification

Morris

Müller-Budack

Ewerth

2020

Lecture Notes in Computer Science

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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.

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“…For the purpose of legal re-use, Sohmen et al [29] provide illustrations of publications of the open access publisher Hindawi 5 . The majority of Hindawi articles is available under the Creative Commons Attribution License, so that they can be used for this type of research.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…Consequently, we follow an autoencoder approach similar to [14], which requires much less labeled data. We gather the necessary samples from open access publications provided by Sohmen et al [29], as explained in detail in the next subsection.…”

Section: Predicting the Abstractness Level Of Image And Textmentioning

confidence: 99%

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“Is This an Example Image?” – Predicting the Relative Abstractness Level of Image and Text

Otto

Holzki

Ewerth

2019

Lecture Notes in Computer Science

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te the feasibility of the approach. Successful multimodal search and retrieval requires the automatic understanding of semantic cross-modal relations, which, however, is still an open research problem. Previous work has suggested the metrics crossmodal mutual information and semantic correlation to model and predict cross-modal semantic relations of image and text. In this paper, we present an approach to predict the (cross-modal) relative abstractness level of a given image-text pair, that is whether the image is an abstraction of the text or vice versa. For this purpose, we introduce a new metric that captures this specific relationship between image and text at the Abstractness Level (ABS). We present a deep learning approach to predict this metric, which relies on an autoencoder architecture that allows us to significantly reduce the required amount of labeled training data. For this, a comprehensive set of publicly available scientific documents has been accumulated. Experimental results on a challenging test set demonstrate the feasibility of the approach.

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Figures in Scientific Open Access Publications

Cited by 7 publications

References 8 publications

Predicting Word Concreteness and Imagery

Predicting Word Concreteness and Imagery

SlideImages: A Dataset for Educational Image Classification

“Is This an Example Image?” – Predicting the Relative Abstractness Level of Image and Text

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