Breaking the interactive bottleneck in multi-class classification with active selection and binary feedback
Multi-class classification schemes typically require human input in the form of precise category names or numbers for each example to be annotated - providing this can be impractical for the user when a large (and possibly unknown) number of categories are present. In this paper, we propose a multi-class active learning model that requires only binary (yes/no type) feedback from the user. For instance, given two images the user only has to say whether they belong to the same class or not. We...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of The 23rd IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010)|
|01_Joshi_Breaking_the_interactive_2010.pdf||774.85 kB||Adobe PDF||Request a copy|
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