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Learning icons appearance similarity

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Abstract

Selecting an optimal set of icons is a crucial step in the pipeline of visual design to structure and navigate through content. However, designing the icons sets is usually a difficult task for which expert knowledge is required. In this work, to ease the process of icon set selection to the users, we propose a similarity metric which captures the properties of style and visual identity. We train a Siamese Neural Network with an on-line dataset of icons organized in visually coherent collections that are used to adaptively sample training data and optimize the training process. As the dataset contains noise, we further collect human-rated information on the perception of icon’s similarity which will be used for evaluating and testing the proposed model. We present several results and applications based on searches, kernel visualizations and optimized set proposals that can be helpful for designers and non-expert users while exploring large collections of icons.

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Notes

  1. https://material.google.com/

  2. https://thenounproject.com/

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Acknowledgments

We want to thank the anonymous reviewers and Adrian Jarabo for their insightful comments on the manuscript. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (CHAMELEON project, grant agreement No 682080).

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Authors and Affiliations

  1. Universidad de Zaragoza, I3A, Zaragoza, Spain

    Manuel Lagunas & Diego Gutierrez

  2. Technicolor, 975 Avenue des Champs Blancs, 35576, Cesson-Sévigné, France

    Elena Garces

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  1. Manuel Lagunas
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  2. Elena Garces
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  3. Diego Gutierrez
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Correspondence to Manuel Lagunas.

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Lagunas, M., Garces, E. & Gutierrez, D. Learning icons appearance similarity. Multimed Tools Appl 78, 10733–10751 (2019). https://doi.org/10.1007/s11042-018-6628-7

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