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Research Projects

Ambiguity Correspondence Reduction in Loosely Labeled Data

Obtaining labeled data in large quantities for model-learning purpose is tedious and time-consuming. In contrast, loosely labeled data where data items are associated with sets of plausible labels are becoming increasingly available today, for example Corel and Flicker. It is an interesting question whether we can push loosely labeled data into more tightly labeled data without help from any supervisory data. One clear advantage of doing this is that improved data with ambiguity correspondence reduced will allow us to directly apply up-to-date supervised learning algorithms for various classification/recognition tasks. .

In this project, we focus on the domain of image annotation and region labeling. we explore the idea of exclusion reasoning to turn the keywords of Corel images into region labels, i.e we associate a keyword to each region/segment in an image. label_image

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