A study of methods for fine-grained object classification of arthropod specimens Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/4b29b8204

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • Object categorization is one of the fundamental topics in computer vision research. Most current work in object categorization aims to discriminate among generic object classes with gross differences. However, many applications require much finer distinctions. This thesis focuses on the design, evaluation and analysis of learning algorithms for fine- grained object classification. The contributions of the thesis are three-fold. First, we introduce two databases of high-resolution images of arthropod specimens we collected to promote the development of highly accurate fine-grained recognition methods. Second, we give a literature review on the development of Bag-of-words (BOW) approaches to image classification and present the stacked evidence tree approach we developed for the fine-grained classification task. We draw connections and analyze differences between those two genres of approaches, which leads to a better understanding about the design of image classification approaches. Third, benchmark results on our two datasets are pre- sented. We further analyze the influence of two important variables on the performance of fine-grained classification. The experiments corroborate our hypotheses that a) high resolution images and b) more aggressive information extraction, such as finer descriptor encoding with large dictionaries or classifiers based on raw descriptors, is required to achieve good fine-grained categorization accuracy.
Resource Type
Date Available
Date Copyright
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
Academic Affiliation
Non-Academic Affiliation
Keyword
Subject
Rights Statement
Peer Reviewed
Language
Replaces
Additional Information
  • description.provenance : Made available in DSpace on 2013-04-04T17:19:02Z (GMT). No. of bitstreams: 1 LinJunyuan2013.pdf: 3350432 bytes, checksum: 3534d5c9e2935f3ced675dad52b4bbee (MD5) Previous issue date: 2013-02-18
  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2013-04-03T17:06:43Z (GMT) No. of bitstreams: 1 LinJunyuan2013.pdf: 3350432 bytes, checksum: 3534d5c9e2935f3ced675dad52b4bbee (MD5)
  • description.provenance : Submitted by Junyuan Lin (linju@onid.orst.edu) on 2013-03-27T08:27:51Z No. of bitstreams: 1 LinJunyuan2013.pdf: 3350432 bytes, checksum: 3534d5c9e2935f3ced675dad52b4bbee (MD5)
  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2013-04-04T17:19:02Z (GMT) No. of bitstreams: 1 LinJunyuan2013.pdf: 3350432 bytes, checksum: 3534d5c9e2935f3ced675dad52b4bbee (MD5)

Relationships

In Administrative Set:
Last modified: 08/04/2017

Downloadable Content

Download PDF
Citations:

EndNote | Zotero | Mendeley

Items