Show simple item record

dc.rights.licenseCC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.
dc.contributorStewart, Charles V
dc.contributorCutler, Barbara M.
dc.contributorGittens, Alex
dc.contributor.authorNouafo Wanko, Stéphane Junior
dc.date.accessioned2021-11-03T09:21:02Z
dc.date.available2021-11-03T09:21:02Z
dc.date.created2021-01-08T15:05:36Z
dc.date.issued2020-08
dc.identifier.urihttps://hdl.handle.net/20.500.13015/2585
dc.descriptionAugust 2020
dc.descriptionSchool of Science
dc.description.abstractThe two approaches that we consider are a classification-based approach and an embedding-based one. We were able to achieve a top-1 accuracy of 83.0% for the classifier and of 80.5% when using embeddings. While both approaches showed good results towards identifying novel individuals, there were drawbacks and benefits to using one over the other. Most importantly, we show that a classification-based approach is most appropriate for quickly learning the weights for the used model. It also consistently performs better overall than the embedding-based approach. When using embeddings however, because of the use of an embedding function to acquire the feature vectors that represent our known individuals, there is the possibility to convert new individuals to known individuals without the need for retraining. We achieve a top-1 accuracy of 76.5% with our embedding approach on newly added individuals with no retraining. We also show that a trained classifier can be converted to an embedding model with no or minimal retraining needed.
dc.description.abstractMany current methods for the identification of individuals of a species do not consider the problem of identifying previously unseen individuals. To be used in a real-world setting, these methods must be able to recognize that all individuals they encounter will not all necessarily be part of the set of individuals the methods were trained to recognize. In this thesis, we explore two different approaches for the identification of new individuals.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectComputer science
dc.titleA study of classification and embedding methods for identifying humpback whales
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid180249
dc.digitool.pid180250
dc.digitool.pid180251
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreeMS
dc.relation.departmentDept. of Computer Science


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.
Except where otherwise noted, this item's license is described as CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.