Image webs : discovering and using object-manifold structure in large-scale image collections
Abstract/Contents
- Abstract
- This dissertation explores a method for data-mining in large image collections with applications to object recognition and dataset visualization. The method builds graphs that preserve the local metric structure of the ideal object-manifolds by linking instances of the same object in different scenes using local image feature matching techniques. A range of image matching techniques are evaluated resulting in a design optimized for the task of preserving local metric structure. Benchmark evaluations on a large dataset show the proposed graph representation is useful for fine-grained semi-supervised object discovery, localization, and annotation tasks. These findings are interesting because fine-grained object recognition for a large number of objects is beyond the reach of the current state-of-the art computer vision algorithms. This work suggests promising directions for data-driven computer vision techniques for automatically organizing massive image collections in which the size of the dataset becomes an advantage rather than a limiting factor.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2013 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Heath, Kyle |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | Guibas, Leonidas J |
| Thesis advisor | Guibas, Leonidas J |
| Thesis advisor | Girod, Bernd |
| Thesis advisor | Li, Fei Fei, 1976- |
| Advisor | Girod, Bernd |
| Advisor | Li, Fei Fei, 1976- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Kyle Heath. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2013. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2013 by Kyle Howard Heath
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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