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Breaking the interactive bottleneck in multi-class classification with active selection and binary feedback

Joshi, Ajay J.; Porikli, Fatih; Papanikolopoulos, Nikolaos


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]

CollectionsANU Research Publications
Date published: 2010
Type: Conference paper
Source: Proceedings of The 23rd IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010)
DOI: 10.1109/CVPR.2010.5540047


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