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Smart learning of logo detection for mobile phone applications

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Abstract

With the advance of mobile phone cameras and broadband networks, gaining access to digital information and services via logo recognition has become of high industrial interest. The fundamental subsystem for logo recognition must be a logo database, whose images link real-world information to specific corporate entities. However, few attempts have been made to create and update such a logo database, i.e., how to automatically collect the latest logos. Moreover, the few existing methods are limited in their application and unattractive in terms of logo detection accuracy and performance overhead. In this article, we describe a practical system for automatic logo extraction. Websites are an optimal source of a huge number of up-to-date logos, and experts can easily find logos from webpages without rendering. For instance, an expert can locate elements with the term “logo” using the websites’ entity names as attribute values, and then download images connected to them. Our system mimics this human behavior to automate logo extraction. Given a website, it learns its entity name and uses that name to locate elements that lead to the logo. Evaluation tests showed that this contextual reasoning significantly contributes to the performance of the system, which achieved high precision with negligible overhead.

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Acknowledgments

This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP). [No. 10041145, Self-Organized Software platform (SoSp) for Welfare Devices].

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Authors and Affiliations

  1. Samsung Electronics Co., Ltd, Banwol-dong, Hwaseong-si, Gyeonggi-do, 445-701, South Korea

    Insoon Jo

  2. School of Electronics Engineering, Kyungpook National University, Sangyeok-dong, Buk-gu, Daegu, 702-701, South Korea

    Im Y. Jung

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  1. Insoon Jo
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  2. Im Y. Jung
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Correspondence to Im Y. Jung.

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Jo, I., Jung, I.Y. Smart learning of logo detection for mobile phone applications. Multimed Tools Appl 75, 13211–13233 (2016). https://doi.org/10.1007/s11042-016-3293-6

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  • DOI: https://doi.org/10.1007/s11042-016-3293-6

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