A content-based image retrieval system for fish taxonomy

  • Authors:

  • Yixin Chen;Henry L. Bart, Jr.;Fei Teng

  • Affiliations:

  • University of New Orleans, New Orleans, LA;Tulane University Museum of Natural History, Belle Chasse, LA;University of New Orleans, New Orleans, LA

  • Venue:
  • Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
  • Year:
  • 2005

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Abstract

It is estimated that less than ten percent of the world's species have been discovered and described. The main reason for the slow pace of new species description is that the science of taxonomy, as traditionally practiced, can be very laborious: taxonomists have to manually gather and analyze data from large numbers of specimens, often from broad geographic areas, and identify the smallest subset of external body characters that uniquely diagnoses the new species as distinct from all its known relatives. The pace of data gathering and analysis in taxonomy can be greatly increased by the development of information technology. The Internet is being used to link taxonomists,taxonomic literature and specimen databases in different parts of the globe, and hence enables the development of tools for remote study of specimens archived as digital images. In this paper, we propose a content-based image retrieval system for taxonomic research. The system has a learning component that can identify representative body shape characters of known species based on digitized landmarks. The system can also provide statistical clues for assisting taxonomists to identify new species or subspecies. The experiments on a taxonomic problem involving species of suckers in the genera Carpiodes demonstrate promising results.