An integrated aurora image retrieval system: AuroraEye

  • Authors:

  • Rong Fu;Xinbo Gao;Xuelong Li;Dacheng Tao;Yongjun Jian;Jie Li;Hongqiao Hu;Huigen Yang

  • Affiliations:

  • School of Electronic Engineering, Xidian University, Xi'an 710071, Shaanxi, China;School of Electronic Engineering, Xidian University, Xi'an 710071, Shaanxi, China;Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, ...;School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N4, Singapore 639798, Singapore;School of Electronic Engineering, Xidian University, Xi'an 710071, Shaanxi, China;School of Electronic Engineering, Xidian University, Xi'an 710071, Shaanxi, China;SOA Key Laboratory for Polar Science, Polar Research Institute of China, Shanghai 200136, China;SOA Key Laboratory for Polar Science, Polar Research Institute of China, Shanghai 200136, China

  • Venue:
  • Journal of Visual Communication and Image Representation
  • Year:
  • 2010

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Abstract

With the digital all-sky imager (ASI) emergence in aurora research, millions of images are captured annually. However, only a fraction of which can be actually used. To address the problem incurred by low efficient manual processing, an integrated image analysis and retrieval system is developed. For precisely representing aurora image, macroscopic and microscopic features are combined to describe aurora texture. To reduce the feature dimensionality of the huge dataset, a modified local binary pattern (LBP) called ALBP is proposed to depict the microscopic texture, and scale-invariant Gabor and orientation-invariant Gabor are employed to extract the macroscopic texture. A physical property of aurora is inducted as region features to bridge the gap between the low-level visual features and high-level semantic description. The experiments results demonstrate that the ALBP method achieves high classification rate and low computational complexity. The retrieval simulation results show that the developed retrieval system is efficient for huge dataset.