Skip to main content

Analysis of Visual Elements in Logo Design

  • Conference paper
Smart Graphics (SG 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8698))

Included in the following conference series:

Abstract

A logo is a mark composed of graph or a combination of text and graph. Typical visual elements in a logo design such as layout, shape, color, composition, and typeface. The graphical mark can exhibit interesting properties by mixing the elements in creative ways. Most previous researches regarding the role of visual elements in logo design are of qualitative nature. In this paper, we propose to incorporate visual feature extraction and analysis algorithms commonly utilized in computer vision to compute proper index and investigate key visual elements in logo design, including complexity, balance and repetition. After analyzing large amount of logos collected from the internet, we find out that most logos are of low complexity, high balance and exhibit minor degree of repetition. We hope that the results obtained in this research serve as a catalyst to motivate further efforts in applying computer vision methods to the area of aesthetics and design.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 34.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 44.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Viola, P., Jones, M.J.: Robust Real-Time Face Detection. International Journal of Computer Vision 57(2), 137–154 (2004)

    Article  Google Scholar 

  2. Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  3. Kisacanin, B., Pavlovic, V., Huang, T.S. (eds.): Real-Time Vision for Human-Computer Interaction. Springer (2005)

    Google Scholar 

  4. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2003)

    Google Scholar 

  5. Stork, D.G.: Computer Vision and Computer Graphics Analysis of Paintings and Drawings: An Introduction to the Literature. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 9–24. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  6. Joshi, D., Datta, R., Luong, Q.T., Fedorovskaya, E., Wang, Z., Li, J., Luo, J.: Aesthetics and Emotions in Images: A Computational Perspective. IEEE Signal Processing Magazine 28(5), 94–115 (2011)

    Article  Google Scholar 

  7. Greenfield: On the Origins of the Term “Computational Aesthetics”. In: Neumann, L., Sbert, M., Gooch, B., Purgathofer, W. (eds.) Computational Aesthetics, pp. 9–12 (2005)

    Google Scholar 

  8. van der Lans, R., Cote, J.A., Cole, C.A., Leong, S.M., Smidts, A., Henderson, P.W., Bluemelhuber, C., Bottomley, P.A., Doyle, J.R., Fedorikhin, A., Moorthy, J., Ramaseshan, B., Schmitt, B.H.: Cross-National Logo Evaluation Analysis: An Individual-Level Approach. Marketing Science 28, 968–985 (2009)

    Article  Google Scholar 

  9. Redies, C., Amirshahi, S.A., Koch, M., Denzler, J.: PHOG-derived Aesthetic Measures Applied to Color Photographs of Artworks, Natural Scenes and Objects. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012 Ws/Demos, Part I. LNCS, vol. 7583, pp. 522–531. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  10. Wilson, A., Chatterjee, A.: The Assessment of Preference for Balance: Introducing a New Test. Empirical Studies of the Arts 23, 165–180 (2005)

    Article  Google Scholar 

  11. Birkhoff, G.D.: Aesthetic Measure. Cambridge Massachusetts’ University Press (1933)

    Google Scholar 

  12. Belongie, S., Malik, J., Puzicha, J.: Shape Matching and Object Recognition Using Shape Contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(24), 509–521 (2002)

    Article  Google Scholar 

  13. Rigau, J., Feixas, M., Sbert, M.: An Information-Theoretic Framework for Image Complexity. In: Computational Aesthetics, pp. 177–184 (2005)

    Google Scholar 

  14. Suzuki, S.: Topological Structural Analysis of Digitized Binary Images by Border Following. Computer Vision, Graphics, and Image Processing 30, 32–46 (1985)

    Article  MATH  Google Scholar 

  15. Rubner, Y., Tomasi, C., Guibas, L.J.: The Earth Mover’s Distance as a Metric for Image Retrieval. International Journal of Computer Vision 40(2), 99–121 (2000)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Liao, WH., Chen, PM. (2014). Analysis of Visual Elements in Logo Design. In: Christie, M., Li, TY. (eds) Smart Graphics. SG 2014. Lecture Notes in Computer Science, vol 8698. Springer, Cham. https://doi.org/10.1007/978-3-319-11650-1_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11650-1_7

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11649-5

  • Online ISBN: 978-3-319-11650-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics