Image features and learning algorithms for biological, generic and social object recognition Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/8s45qd92s

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • Automated recognition of object categories in images is a critical step for many real-world computer vision applications. Interest region detectors and region descriptors have been widely employed to tackle the variability of objects in pose, scale, lighting, texture, color, and so on. Different types of object recognition problems usually require different image features and corresponding learning algorithms. This dissertation focuses on the design, evaluation and application of new image features and learning algorithms for the recognition of biological, generic and social objects. The first part of the dissertation introduces a new structure-based interest region detector called the principal curvature-based region detector (PCBR) which detects stable watershed regions that are robust to local intensity perturbations. This detector is specifically designed for region detection for biological objects. Several recognition architectures are then developed that fuse visual information from disparate types of image features for the categorization of complex objects. The described image features and learning algorithms achieve excellent performance on the difficult stonefly larvae dataset. The second part of the dissertation presents studies of methods for visual codebook learning and their application to object recognition. The dissertation first introduces the methodology and application of generative visual codebooks for stonefly recognition and introduces a discriminative evaluation methodology based on a maximum mutual information criterion. Then a new generative/discriminative visual codebook learning algorithm, called iterative discriminative clustering (IDC), is presented that refines the centers and the shapes of the generative codewords for improved discriminative power. It is followed by a novel codebook learning algorithm that builds multiple codebooks that are non-redundant in discriminative power. All these visual codebook learning algorithms achieve high performance on both biological and generic object recognition tasks. The final part of the dissertation describes a socially-driven clothes recognition system for an intelligent fitting-room system. The dissertation presents the results of a user study to identify the key factors for clothes recognition. It then describes learning algorithms for recognizing these key factors from clothes images using various image features. The clothes recognition system successfully enables automated social fashion information retrieval for an enhanced clothes shopping experience.
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
Language
Replaces
Additional Information
  • description.provenance : Approved for entry into archive by Linda Kathman(linda.kathman@oregonstate.edu) on 2009-04-06T17:27:45Z (GMT) No. of bitstreams: 1 Dissertation_WeiZhang_930560279.pdf: 6137166 bytes, checksum: e29b2d71eb55ff07d3c2a1ae83c3a4ec (MD5)
  • description.provenance : Submitted by Wei Zhang (zhangwe@onid.orst.edu) on 2009-03-24T23:18:07Z No. of bitstreams: 1 Dissertation_WeiZhang_930560279.pdf: 6137166 bytes, checksum: e29b2d71eb55ff07d3c2a1ae83c3a4ec (MD5)
  • description.provenance : Made available in DSpace on 2009-04-06T17:27:45Z (GMT). No. of bitstreams: 1 Dissertation_WeiZhang_930560279.pdf: 6137166 bytes, checksum: e29b2d71eb55ff07d3c2a1ae83c3a4ec (MD5)
  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2009-04-01T16:19:00Z (GMT) No. of bitstreams: 1 Dissertation_WeiZhang_930560279.pdf: 6137166 bytes, checksum: e29b2d71eb55ff07d3c2a1ae83c3a4ec (MD5)

Relationships

In Administrative Set:
Last modified: 10/18/2017

Downloadable Content

Download PDF
Citations:

EndNote | Zotero | Mendeley

Items