A Content-Based Image Retrieval System for Fish Taxonomy

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 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 can be greatly increased by the information technology. In this paper, we propose a content-based image retrieval system for taxonomic research. The system can identify representative body shape characters of known species based on digitized landmarks and 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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year

2006

Author dc:creator

• Teng, Fei

Contributors dc:contributor

• Fu, Bin
• Deng, Jing
• Chen, Yixin

• Content-based image retrieval
• Shape analysis
• Feature selection
• Image classification
• Taxonomic research

Repository record dc:identifier

https://scholarworks.uno.edu/td/377

OAI identifier oai:identifier

oai:scholarworks.uno.edu:td-1398