2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIVMSP-31.2
Paper Title Rotation Invariance Analysis of Local Convolutional Features in Image Retrieval
Authors Longjiao Zhao, NAGOYA UNIVERSITY, Japan; Yu Wang, Jien Kato, Ritsumeikan University, Japan
SessionIVMSP-31: Applications 3
LocationGather.Town
Session Time:Friday, 11 June, 14:00 - 14:45
Presentation Time:Friday, 11 June, 14:00 - 14:45
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVSMR] Image & Video Sensing, Modeling, and Representation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recently, local features computed using convolutional neural networks (CNNs) show good performance to image retrieval. However, the local convolutional features obtained by the CNNs (LC features) are inherently sensitive to rotation perturbations. This leads to miss-judgements in retrieval tasks. In this work, our objective is to enhance the robustness of LC features against image rotation. To do this, we conduct a thorough experimental evaluation of two candidate anti-rotation strategies (in-model data augmentation, and post-model feature augmentation), over two kinds of rotation attacks (dataset attack and query attack). We end up a series of good practices with steady quantitative supports, which lead to the best strategy for computing LC features with high rotation invariance in image retrieval.