Criteria Sliders: Learning Continuous Database Criteria via Interactive Ranking

James Tompkin, Kwang In Kim, Hanspeter Pfister, Christian Theobalt

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

3 Citations (SciVal)


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.
Original languageEnglish
Title of host publicationProc. British Machine Vision Conference, 2017
Publication statusPublished - 2017
EventBMVC 2017: The British Machine Vision Conference -
Duration: 4 Sept 20177 Sept 2017


ConferenceBMVC 2017: The British Machine Vision Conference


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