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Environmental Microbiological Content-Based Image Retrieval System Using Internal Structure Histogram

  • Yan Ling ZouEmail author
  • Chen Li
  • Zeyd Boukhers
  • Kimiaki Shirahama
  • Tao Jiang
  • Marcin Grzegorzek
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 403)

Abstract

Environmental Microbiology (EM) is an important scientific field, which investigates the ecological usage of different microorganisms. Traditionally, researchers look for the information of microorganisms by checking references or consulting experts. However, these methods are time-consuming and not effective. To increase the effectiveness of EM information search, we propose a novel approach to aid the information searching work using Content-based Image Retrieval (CBIR). First, we use an microorganism image as input data. Second, image segmentation technique is applied to obtain the shape of the microorganism. Third, we extract shape feature from the segmented shape to represent the microorganism. Especially, we use a contour-based shape feature called Internal Structure Histogram (ISH) to describe the shape, which can use angles defined on the shape contour to build up a histogram and represent the structure of the microorganism. Finally, we use Euclidean distances between each ISHs to measure the similarity of different EM images in the retrieval task, and use Average Precision (AP) to evaluate the retrieval result. The experimental result shows the effectiveness and potential of our EM-CBIR system.

Keywords

Environmental microbiology Content-based image retrieval Image segmentation Internal structure histogram 

Notes

Acknowledgments

We thank Program of Study Abroad for Young Scholar (supported by Chengdu University of Information Technology (CUIT)), China Scholarship Council, Project (No. Y2013106) Supported by the Teaching Research Foundation of CUIT, Project (No. KYTZ201410) Supported by the Scientific Research Foundation of CUIT, and Project (No. 2015GZ0197, 2015GZ0304) Supported by Scientific Research Fund of Sichuan Provincial Science & Technology Department to support this research work. We also thank Prof. Dr. Beihai Zhou and M.Sc. Fangshu Ma from University of Science and Technology Beijing (USTB) for their great help. Also, we thank Cathrin Warnke from the University of Siegen for her proof reading.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Yan Ling Zou
    • 1
    • 2
    Email author
  • Chen Li
    • 2
  • Zeyd Boukhers
    • 2
  • Kimiaki Shirahama
    • 2
  • Tao Jiang
    • 1
  • Marcin Grzegorzek
    • 2
  1. 1.Chengdu University of Information TechnologyChengduChina
  2. 2.Pattern Recognition GroupUniversity of SiegenSiegenGermany

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