Local Descriptors without Orientation Normalization to Enhance Landmark Regconition

  • Dai-Duong Truong
  • Chau-Sang Nguyen Ngoc
  • Vinh-Tiep Nguyen
  • Minh-Triet Tran
  • Anh-Duc Duong
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 244)


Derive from practical needs, especially in tourism industry; landmark recognition is an interesting and challenging problem on mobile devices. To obtain the robustness, landmarks are described by local features with many levels of invariance among which rotation invariance is commonly considered an important property. We propose to eliminate orientation normalization for local visual descriptors to enhance the accuracy in landmark recognition problem. Our experiments show that with three different widely used descriptors, including SIFT, SURF, and BRISK, our idea can improve the recognition accuracy from 2.3 to 12.6% while reduce the feature extraction time from 2.5 to 11.1%. This suggests a simple yet efficient method to boost the accuracy with different local descriptors with orientation normalization in landmark recognition applications.


Visual Word Local Descriptor Rotation Invariance Orientation Normalization Discriminative Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dai-Duong Truong
    • 1
  • Chau-Sang Nguyen Ngoc
    • 1
  • Vinh-Tiep Nguyen
    • 1
  • Minh-Triet Tran
    • 1
  • Anh-Duc Duong
    • 2
  1. 1.Faculty of Information TechnologyUniversity of Science, VNU-HCMHo Chi MinhVietnam
  2. 2.University of Information Technology, VNU-HCMHo Chi MinhVietnam

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