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Famous Companies Use More Letters in Logo: A Large-Scale Analysis of Text Area in Logo

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Document Analysis and Recognition – ICDAR 2021 Workshops (ICDAR 2021)

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Abstract

This paper analyzes a large number of logo images from the LLD-logo dataset, by recent deep learning-based techniques, to understand not only design trends of logo images and but also the correlation to their owner company. Especially, we focus on three correlations between logo images and their text areas, between the text areas and the number of followers on Twitter, and between the logo images and the number of followers. Various findings include the weak positive correlation between the text area ratio and the number of followers of the company. In addition, deep regression and deep ranking methods can catch correlations between the logo images and the number of followers.

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Notes

  1. 1.

    There are several variations in the logo type classification. For example, in [1], three types are called “text-based logo”, “iconic or symbolic logo”, and “mixed logo”, respectively.

  2. 2.

    Not available now. According to [15], it contained 123 logo images.

  3. 3.

    The number of followers is provided in the file LLD-logo.hdf5, which is provided with LLD-logo image data. Precisely, the resource of meta_data/twitter/user_objects in this hdf5 file contains the followers_count data, which corresponds to the number of followers.

  4. 4.

    For the logarithmic plot, we exclude the companies with zero follower. The number of such companies is around 1,000 and thus no serious effect in our discussion.

  5. 5.

    As we will see later, the text area ratio sometimes exceeds 100%. There are several reasons behind it; one major reason is the ambiguity of the whole logo’s bounding box and the individual text areas’ bounding boxes. If the latter slightly becomes the former, the ratio exceeds 100%. Some elaborated post-processing might reduce those cases; however, they do not significantly affect our median-based analysis.

  6. 6.

    Recall that each distribution is normalized. As noted before, logotypes are just 4%, whereas logo symbols are 26%. Thus, as the absolute numbers, the famous companies use more logo symbols than logotypes.

References

  1. Adîr, G., Adîr, V., Pascu, N.E.: Logo design and the corporate identity. Procedia. Soc. Behav. Sci. 51, 650–654 (2012)

    Article  Google Scholar 

  2. Aljalbout, E., Golkov, V., Siddiqui, Y., Strobel, M., Cremers, D.: Clustering with deep learning: taxonomy and new methods. arXiv preprint arXiv:1801.07648 (2018)

  3. Ardon, S., et al.: Spatio-temporal analysis of topic popularity in twitter. arXiv preprint arXiv:1111.2904 (2011)

  4. Baek, Y., Lee, B., Han, D., Yun, S., Lee, H.: Character region awareness for text detection. In: CVPR (2019)

    Google Scholar 

  5. Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on twitter. In: ACM WSDM (2011)

    Google Scholar 

  6. Burges, C., et al.: Learning to rank using gradient descent. In: ICML (2005)

    Google Scholar 

  7. Caron, M., Bojanowski, P., Joulin, A., Douze, M.: Deep clustering for unsupervised learning of visual features. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 139–156. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_9

    Chapter  Google Scholar 

  8. Guo, J., et al.: A deep look into neural ranking models for information retrieval. Inf. Proc. Manag. 57(6), 102067 (2020)

    Google Scholar 

  9. Karamatsu, T., Suehiro, D., Uchida, S.: Logo design analysis by ranking. In: ICDAR (2019)

    Google Scholar 

  10. Lerman, K., Ghosh, R., Surachawala, T.: Social contagion: an empirical study of information spread on digg and twitter follower graphs. arXiv preprint arXiv:1202.3162 (2012)

  11. Luffarelli, J., Mukesh, M., Mahmood, A.: Let the logo do the talking: the influence of logo descriptiveness on brand equity. J. Marketing Res. 56(5), 862–878 (2019)

    Article  Google Scholar 

  12. Luffarelli, J., Stamatogiannakis, A., Yang, H.: The visual asymmetry effect: An interplay of logo design and brand personality on brand equity. J. Marketing Res. 56(1), 89–103 (2019)

    Article  Google Scholar 

  13. Ma, N., Volkov, A., Livshits, A., Pietrusinski, P., Hu, H., Bolin, M.: An universal image attractiveness ranking framework. In: WACV (2019)

    Google Scholar 

  14. Min, E., Guo, X., Liu, Q., Zhang, G., Cui, J., Long, J.: A survey of clustering with deep learning: from the perspective of network architecture. IEEE Access 6, 39501–39514 (2018)

    Article  Google Scholar 

  15. Neumann, J., Samet, H., Soffer, A.: Integration of local and global shape analysis for logo classification. Patt. Recog. Lett. 23(12), 1449–1457 (2002)

    Article  Google Scholar 

  16. Sage, A., Agustsson, E., Timofte, R., Van Gool, L.: Logo synthesis and manipulation with clustered generative adversarial networks. In: CVPR (2018)

    Google Scholar 

  17. Stringhini, G., et al.: Follow the green: growth and dynamics in twitter follower markets. In: Internet Measurement Conference (2013)

    Google Scholar 

  18. Su, H., Gong, S., Zhu, X.: WebLogo-2M: scalable logo detection by deep learning from the web. In: ICCVW (2017)

    Google Scholar 

  19. Sundar, A., Noseworthy, T.J.: Place the logo high or low? Using conceptual metaphors of power in packaging design. J. Marketing 78(5), 138–151 (2014)

    Article  Google Scholar 

  20. Tang, H., Chen, K., Jia, K.: Unsupervised domain adaptation via structurally regularized deep clustering. In: CVPR (2020)

    Google Scholar 

  21. Wang, J., et al.: Logo-2k+: a large-scale logo dataset for scalable logo classification. In: AAAI (2020)

    Google Scholar 

  22. Zhan, X., Xie, J., Liu, Z., Ong, Y.S., Loy, C.C.: Online deep clustering for unsupervised representation learning. In: CVPR (2020)

    Google Scholar 

  23. Zhao, W.: A brief analysis on the new trend of logo design in the digital information era. In: ESSAEME (2017)

    Google Scholar 

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Acknowledgment

This work was supported by JSPS KAKENHI Grant Number JP17H06100.

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Correspondence to Shintaro Nishi .

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Nishi, S., Kadota, T., Uchida, S. (2021). Famous Companies Use More Letters in Logo: A Large-Scale Analysis of Text Area in Logo. In: Barney Smith, E.H., Pal, U. (eds) Document Analysis and Recognition – ICDAR 2021 Workshops. ICDAR 2021. Lecture Notes in Computer Science(), vol 12916. Springer, Cham. https://doi.org/10.1007/978-3-030-86198-8_8

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  • DOI: https://doi.org/10.1007/978-3-030-86198-8_8

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