Taxonomic classification has always been important to the study of any biological system. Many biological species will go unclassified and become lost forever at the current rate of classification. The current state of computer technology makes image storage and retrieval possible on a global level. As a result, computer-aided taxonomy is now possible. Content based image retrieval techniques utilize visual features of the image for classification. By utilizing image content and computer technology, the gap between taxonomic classification and species destruction is shrinking. This content based study utilizes the Fourier Descriptors of fifteen known landmark features on three Carpiodes species: C.carpio, C.velifer, and C.cyprinus. Classification analysis involves both unsupervised and supervised machine learning algorithms. Fourier Descriptors of the fifteen known landmarks provide for strong classification power on image data. Feature reduction analysis indicates feature reduction is possible. This proves useful for increasing generalization power of classification.
","abstract_html":"<p>Taxonomic classification has always been important to the study of any biological system. Many biological species will go unclassified and become lost forever at the current rate of classification. The current state of computer technology makes image storage and retrieval possible on a global level. As a result, computer-aided taxonomy is now possible. Content based image retrieval techniques utilize visual features of the image for classification. By utilizing image content and computer technology, the gap between taxonomic classification and species destruction is shrinking. This content based study utilizes the Fourier Descriptors of fifteen known landmark features on three Carpiodes species: C.carpio, C.velifer, and C.cyprinus. Classification analysis involves both unsupervised and supervised machine learning algorithms. Fourier Descriptors of the fifteen known landmarks provide for strong classification power on image data. Feature reduction analysis indicates feature reduction is possible. This proves useful for increasing generalization power of classification.</p>","abstract_has_math":false,"creators":["Trahan, Patrick"],"institution":null,"degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Zhu, Dongxiao","Summa, Christopher","Taylor, Christopher"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2009,"date_issued":"2009-08-06T07:00:00Z","date_published":"2009-08-06T07:00:00Z","updated_at":"2026-07-24T05:29:02Z","subjects":["Content-Based Image Retrieval","Alpha Taxonomy","Beta Taxonomy","Gamma Taxonomy","Principal Component Analysis","K-Means Clustering","Hierarchical Clustering","KNearest Neighbor","Support Vector Machine","Random Forest","Quadratic Discriminant Analysis","Feature Reduction","Variable Importance"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uno.edu/td/1085","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhu, Dongxiao","Summa, Christopher","Taylor, Christopher"]},{"key":"dc:creator","label":"Author","values":["Trahan, Patrick"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Content-Based Image Retrieval","Alpha Taxonomy","Beta Taxonomy","Gamma Taxonomy","Principal Component Analysis","K-Means Clustering","Hierarchical Clustering","KNearest Neighbor","Support Vector Machine","Random Forest","Quadratic Discriminant Analysis","Feature Reduction","Variable Importance"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uno.edu/td/1085"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Taxonomic classification has always been important to the study of any biological system. Many biological species will go unclassified and become lost forever at the current rate of classification. The current state of computer technology makes image storage and retrieval possible on a global level. As a result, computer-aided taxonomy is now possible. Content based image retrieval techniques utilize visual features of the image for classification. By utilizing image content and computer technology, the gap between taxonomic classification and species destruction is shrinking. This content based study utilizes the Fourier Descriptors of fifteen known landmark features on three Carpiodes species: C.carpio, C.velifer, and C.cyprinus. Classification analysis involves both unsupervised and supervised machine learning algorithms. Fourier Descriptors of the fifteen known landmarks provide for strong classification power on image data. Feature reduction analysis indicates feature reduction is possible. This proves useful for increasing generalization power of classification.
"]},{"key":"dc:title","label":"Title","values":["Classification of Carpiodes Using Fourier Descriptors: A Content Based Image Retrieval Approach"]}]}],"canonical_facts":{"dc:contributor":["Zhu, Dongxiao","Summa, Christopher","Taylor, Christopher"],"dc:creator":["Trahan, Patrick"],"dc:description.abstract":["Taxonomic classification has always been important to the study of any biological system. Many biological species will go unclassified and become lost forever at the current rate of classification. The current state of computer technology makes image storage and retrieval possible on a global level. As a result, computer-aided taxonomy is now possible. Content based image retrieval techniques utilize visual features of the image for classification. By utilizing image content and computer technology, the gap between taxonomic classification and species destruction is shrinking. This content based study utilizes the Fourier Descriptors of fifteen known landmark features on three Carpiodes species: C.carpio, C.velifer, and C.cyprinus. Classification analysis involves both unsupervised and supervised machine learning algorithms. Fourier Descriptors of the fifteen known landmarks provide for strong classification power on image data. Feature reduction analysis indicates feature reduction is possible. This proves useful for increasing generalization power of classification.
"],"dc:identifier":["https://scholarworks.uno.edu/td/1085"],"dc:subject":["Content-Based Image Retrieval","Alpha Taxonomy","Beta Taxonomy","Gamma Taxonomy","Principal Component Analysis","K-Means Clustering","Hierarchical Clustering","KNearest Neighbor","Support Vector Machine","Random Forest","Quadratic Discriminant Analysis","Feature Reduction","Variable Importance"],"dc:title":["Classification of Carpiodes Using Fourier Descriptors: A Content Based Image Retrieval Approach"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."]},"updated_at":"2026-07-24T05:29:02Z"}