DocumentCode :
3672547
Title :
Efficient label collection for unlabeled image datasets
Author :
Maggie Wigness;Bruce A. Draper;J. Ross Beveridge
Author_Institution :
Colorado State University, Fort Collins, USA
fYear :
2015
fDate :
6/1/2015 12:00:00 AM
Firstpage :
4594
Lastpage :
4602
Abstract :
Visual classifiers are part of many applications including surveillance, autonomous navigation and scene understanding. The raw data used to train these classifiers is abundant and easy to collect but lacks labels. Labels are necessary for training supervised classifiers, but the labeling process requires significant human effort. Techniques like active learning and group-based labeling have emerged to help reduce the labeling workload. However, the possibility of collecting label noise affects either the efficiency of these systems or the performance of the trained classifiers. Further, many introduce latency by iteratively retraining classifiers or re-clustering data. We introduce a technique that searches for structural change in hierarchically clustered data to identify a set of clusters that span a spectrum of visual concept granularities. This allows us to efficiently label clusters with less label noise and produce high performing classifiers. The data is hierarchically clustered only once, eliminating latency during the labeling process. Using benchmark data we show that collecting labels with our approach is more efficient than existing labeling techniques, and achieves higher classification accuracy. Finally, we demonstrate the speed and efficiency of our system using real-world data collected for an autonomous navigation task.
Keywords :
Noise
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on
Electronic_ISBN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2015.7299090
Filename :
7299090
Link To Document :
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