Research

Criteria Sliders: Learning Continuous Database Criteria via Interactive Ranking


Reference:

Tompkin, J., Kim, K. I., Pfister, H. and Theobalt, C., 2017. Criteria Sliders: Learning Continuous Database Criteria via Interactive Ranking. In: BMVC 2017: The British Machine Vision Conference, 2017-09-04 - 2017-09-07.

Related documents:

This repository does not currently have the full-text of this item.
You may be able to access a copy if URLs are provided below. (Contact Author)

Abstract

Large databases are often organized by hand-labeled metadata—or criteria—which are expensive to collect. We can use unsupervised learning to model database variation, but these models are often high dimensional, complex to parameterize, or require expert knowledge. We learn low-dimensional continuous criteria via interactive ranking, so that the novice user need only describe the relative ordering of examples. This is formed as semi-supervised label propagation in which we maximize the information gained from a limited number of examples. Further, we actively suggest data points to the user to rank in a more informative way than existing work. Our efficient approach allows users to interactively organize thousands of data points along 1D and 2D continuous sliders. We experiment with databases of imagery and geometry to demonstrate that our tool is useful for quickly assessing and organizing the content of large databases.

Details

Item Type Conference or Workshop Items (UNSPECIFIED)
CreatorsTompkin, J., Kim, K. I., Pfister, H. and Theobalt, C.
DepartmentsFaculty of Science > Computer Science
StatusPublished
ID Code56530

Export

Actions (login required)

View Item