Functional map networks for the joint analysis of image and shape collections
Abstract/Contents
- Abstract
- In many machine learning and computer vision problems, data are often easy and cheap to acquire but very expensive to label. For example, it is effortless to collect a large amount of images of the same object using image search engines, but annotating them about where the objects are costs much more work of a skilled human agent. If we can build relationships between data, abundant information can be transported from labeled ones to unlabeled ones. In our work, we use a novel representation of the relationships between images or shapes, called functional maps. Unlike point-based maps, functional maps build correspondences between functions over images or shapes based on their local properties. We also propose a functional map network to jointly analyze collections of images or shapes. Each image/shape is a node in the network and each edge connecting two images/shapes is associated with the map between them. Finally, we proposed three applications of the map network. First, given a collection of images sharing one similar object, segmentations are transferred among the images via functional maps, and the segmentation for the common object emerges if the map network is cycle-consistent. Second, we extend the functional map networks to handle multi-class joint image segmentation, which is more challenging and requires partial similarity constraint because each common object may only appear in a subset of images. Third, we build a functional map network among 3D shapes for joint shape segmentation. Maps between shapes are regularized and improved by partial cycle-consistency and the shared structure across the collection is discovered, corresponding to the meaningful shape parts. For all applications, experimental results on various benchmark data sets are provided to demonstrate the effectiveness of the proposed approaches.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2015 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Wang, Fan |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | Guibas, Leonidas J |
| Thesis advisor | Guibas, Leonidas J |
| Thesis advisor | Girod, Bernd |
| Thesis advisor | Leskovec, Jurij |
| Advisor | Girod, Bernd |
| Advisor | Leskovec, Jurij |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Fan Wang. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2015. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2015 by Fan Wang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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