• DocumentCode
    3000717
  • Title

    Trademark image retrieval based on scale, rotation, translation invariant features

  • Author

    Tien Dung Nguyen ; Huu Hiep Hai Nguyen ; Thanh Ha Le

  • Author_Institution
    Univ. of Preventing & Fighting Fire, Hanoi, Vietnam
  • fYear
    2013
  • fDate
    10-13 Nov. 2013
  • Firstpage
    282
  • Lastpage
    285
  • Abstract
    Trademark registration offices or authorities have been bombarded with requests from enterprises. These authorities face a great deal of difficulties in protecting enterprises´ rights such as copyright, license, or uniqueness of logo or trademark since they have only conventional clustering. Urgent and essential need for sufficient automatic trademark image retrieval system, therefore, is entirely worth thorough research. In this paper, we propose a novel trademark image retrieval method in which the input trademark image is first separated into dominant visual shape images then a feature vector for each shape image which is scale-, rotation-, and translation- invariant is created. Finally, a similarity measure between two trademarks is calculated based on these feature vectors. Given a query trademark image, retrieval procedure is carried out by taking the most five similar trademark images in a predefined trademark. Various experiments are conducted to mimic the many types of trademark copying.
  • Keywords
    feature extraction; image retrieval; pattern clustering; trademarks; automatic trademark image retrieval system; conventional clustering; dominant visual shape images; feature vector; input trademark image; query trademark image; rotation invariant feature; scale invariant feature; similarity measure; trademark copying; translation invariant feature; Accuracy; Feature extraction; Image retrieval; Shape; Trademarks; Vectors; feature vectors; mirror- invariant; rotation; scale; trademark image; trademark image retrieval; translation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2013 IEEE RIVF International Conference on
  • Conference_Location
    Hanoi
  • Print_ISBN
    978-1-4799-1349-7
  • Type

    conf

  • DOI
    10.1109/RIVF.2013.6719908
  • Filename
    6719908