The MAMe Dataset: On the relevance of High Resolution and Variable Shape image properties

• URL: http://arxiv.org/abs/2007.13693v3
• Date: Thu, 20 May 2021 10:57:06 GMT
• Title: The MAMe Dataset: On the relevance of High Resolution and Variable Shape image properties
• Authors: Ferran Par\'es, Anna Arias-Duart, Dario Garcia-Gasulla, Gema Campo-Franc\'es, Nina Viladrich, Eduard Ayguad\'e, Jes\'us Labarta
• Abstract summary: We introduce the MAMe dataset, an image classification dataset with remarkable high resolution and variable shape properties. The MAMe dataset contains thousands of artworks from three different museums.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In the image classification task, the most common approach is to resize all images in a dataset to a unique shape, while reducing their precision to a size which facilitates experimentation at scale. This practice has benefits from a computational perspective, but it entails negative side-effects on performance due to loss of information and image deformation. In this work we introduce the MAMe dataset, an image classification dataset with remarkable high resolution and variable shape properties. The goal of MAMe is to provide a tool for studying the impact of such properties in image classification, while motivating research in the field. The MAMe dataset contains thousands of artworks from three different museums, and proposes a classification task consisting on differentiating between 29 mediums (i.e. materials and techniques) supervised by art experts. After reviewing the singularity of MAMe in the context of current image classification tasks, a thorough description of the task is provided, together with dataset statistics. Experiments are conducted to evaluate the impact of using high resolution images, variable shape inputs and both properties at the same time. Results illustrate the positive impact in performance when using high resolution images, while highlighting the lack of solutions to exploit variable shapes. An additional experiment exposes the distinctiveness between the MAMe dataset and the prototypical ImageNet dataset. Finally, the baselines are inspected using explainability methods and expert knowledge, to gain insights on the challenges that remain ahead.

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