Detection of the Innovative Logotypes on the Web Pages

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10246)


The aim of this study was to describe a found method for detection of logotypes that indicate innovativeness of companies, where the images originate from their Internet domains. For this purpose, we elaborated a system that covers a supervised and heuristic approach to construct a reference dataset for each logotype category that is utilized by the logistic regression classifiers to recognize a logotype category. We proposed the approach that uses one-versus-the-rest learning strategy to learn the logistic regression classification models to recognize the classes of the innovative logotypes. Thanks to this we can detect whether a given company’s Internet domain contains a innovative logotype or not. Moreover, we find a way to construct a simple and small dimension of feature space that is utilized by the image recognition process. The proposed feature space of logotype classification models is based on the weights of images similarity and the textual data of the images that are received from HTMLs ALT tags.


Logotypes classification Logotypes recognition Images classification Images matching Images feature construction Feature construction 


  1. 1.
    OECD/Eurostat Oslo Manual.
  2. 2.
    Baratis, E., Petrakis, E., Milios, E.: Automatic website summarization by image content: a case study with logo and trademark images. IEEE Trans. Knowl. Data Eng. 20(9), 1195–1204 (2008)CrossRefGoogle Scholar
  3. 3.
    Boia, R., Florea, C., Florea, L., Dogaru, R.: Logo localization and recognition in natural images using homographic class graphs. Mach. Vis. Appl. 27(2), 287–301 (2016)CrossRefGoogle Scholar
  4. 4.
    Cesarini, F., Francesconi, E., Gori, M., Marinai, S., Sheng, J., Soda, G.: A neural-based architecture for spot-noisy logo recognition. In: Proceedings of the Fourth International Conference 1997 on Document Analysis and Recognition, vol. 1, pp. 175–179 (1997)Google Scholar
  5. 5.
    Christoph, Z.: Implementation and Benchmarking of Perceptual Image Hash Functions, 1st edn. Standard, Cincinnati (2010)Google Scholar
  6. 6.
    Cyganek, B.: Hybrid ensemble of classifiers for logo and trademark symbols recognition. Soft Comput. 19(12), 3413–3430 (2015)CrossRefGoogle Scholar
  7. 7.
    Escalera, S., Fornes, A., Pujol, O., Escudero, A., Radeva, P.: Circular blurred shape model for symbol spotting in documents. In: ICIP (2009)Google Scholar
  8. 8.
    Farajzadeh, N.: Exemplar-based logo and trademark recognition. Mach. Vis. Appl. 26(6), 791–805 (2015)CrossRefGoogle Scholar
  9. 9.
    Fauzi, F., Belkhatir, M.: Image understanding and the web: a state-of-the-art review. J. Intell. Inf. Syst. 43(2), 271–306 (2014)CrossRefGoogle Scholar
  10. 10.
    Kesidis, A., Karatzas, D.: Logo and trademark recognition. In: Doermann, D., Tombre, K. (eds.) Handbook of Document Image Processing and Recognition, pp. 591–646. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  11. 11.
    Kotsemir, M.N. Abroskin, A., Meissner, D.: Innovation Concepts and Typology An Evolutionary Discussion. Higher School of Economics Research (2013)Google Scholar
  12. 12.
    Li, Y., Shapiro, L., Bilmes, J.: A generative/discriminative learning algorithm for image classification. In: Computer Vision 2005, ICCV 2005 (2005)Google Scholar
  13. 13.
    Mirończuk, M., Protasiewicz, J.: A diversified classification committee for recognition of innovative internet domains. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. CCIS, vol. 613, pp. 368–383. Springer, Cham (2016)CrossRefGoogle Scholar
  14. 14.
    Petrakis, E.G.M., Voutsakis, E., Milios, E.E.: Searching for logo and trademark images on the web. In: Proceedings of the 6th ACM ICIVR, pp. 541–548 (2007)Google Scholar
  15. 15.
    Psyllos, A.P., Anagnostopoulos, C.N.E., Kayafas, E.: Vehicle logo recognition using a SIFT-based enhanced matching scheme. IEEE Trans. Intell. Transp. Syst. 11(2), 322–328 (2010)CrossRefGoogle Scholar
  16. 16.
    Romberg, S., Pueyo, L.G., Lienhart, R., van Zwol, R.: Scalable logo recognition in real-world images. In: Proceedings ICMR, pp. 25:1–25:8 (2011)Google Scholar
  17. 17.
    Rusinol, M., Llados, J.: Logo spotting by a bag-of-words approach for document categorization. In: ICDAR 2009, pp. 111–115, July 2009Google Scholar
  18. 18.
    Sammut, C., Webb, G.I.: Encyclopedia of Machine Learning, 1st edn. Springer Publishing Company, Inc., Heidelberg (2011)zbMATHGoogle Scholar
  19. 19.
    Voutsakis, E., Petrakis, E., Milios, E.: Weighted link analysis for logo and trademark image retrieval on the web. In: Web Intelligence, pp. 581–585 (2005)Google Scholar
  20. 20.
    Wang, F., Qi, S., Gao, G., Zhao, S., Wang, X.: Logo information recognition in large-scale social media data. Multimedia Syst. 22(1), 63–73 (2016)CrossRefGoogle Scholar
  21. 21.
    Wang, H., Chen, Y.: Logo detection in document images based on boundary extension of feature rectangles. In: Document Analysis and Recognition 2009, pp. 1335–1339, July 2009Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Laboratory of Intelligent Information SystemsNational Information Processing InstituteWarsawPoland

Personalised recommendations