Chapter

Computer Vision – ECCV 2008

Volume 5305 of the series Lecture Notes in Computer Science pp 116-129

Searching the World’s Herbaria: A System for Visual Identification of Plant Species

  • Peter N. BelhumeurAffiliated withDept. Of Computer Science, Columbia University
  • , Daozheng ChenAffiliated withDept. Of Computer Science, University of Maryland
  • , Steven FeinerAffiliated withDept. Of Computer Science, Columbia University
  • , David W. JacobsAffiliated withDept. Of Computer Science, University of Maryland
  • , W. John KressAffiliated withDepartment Of Botany, National Museum of Natural History, Smithsonian Institution
  • , Haibin LingAffiliated withInformation Science and Technology Center, Temple University
  • , Ida LopezAffiliated withDepartment Of Botany, National Museum of Natural History, Smithsonian Institution
  • , Ravi RamamoorthiAffiliated withDept. Of Computer Science, Columbia University
  • , Sameer SheoreyAffiliated withDept. Of Computer Science, University of Maryland
    • , Sean WhiteAffiliated withDept. Of Computer Science, Columbia University
    • , Ling ZhangAffiliated withDepartment Of Botany, National Museum of Natural History, Smithsonian Institution

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Abstract

We describe a working computer vision system that aids in the identification of plant species. A user photographs an isolated leaf on a blank background, and the system extracts the leaf shape and matches it to the shape of leaves of known species. In a few seconds, the system displays the top matching species, along with textual descriptions and additional images. This system is currently in use by botanists at the Smithsonian Institution National Museum of Natural History. The primary contributions of this paper are: a description of a working computer vision system and its user interface for an important new application area; the introduction of three new datasets containing thousands of single leaf images, each labeled by species and verified by botanists at the US National Herbarium; recognition results for two of the three leaf datasets; and descriptions throughout of practical lessons learned in constructing this system.