Paper Info
Title
The Impact of Typicality for Informative Representative Selection
Abstract
In computer vision, selection of the most informative samples from a huge pool of training data in order to learn a good recognition model is an active research problem. Furthermore, it is also useful to reduce the annotation cost, as it is time consuming to annotate unlabeled samples. In this paper, motivated by the theories in data compression, we propose a novel sample selection strategy which exploits the concept of typicality from the domain of information theory. Typicality is a simple and powerful technique which can be applied to compress the training data to learn a good classification model. In this work, typicality is used to identify a subset of the most informative samples for labeling, which is then used to update the model using active learning. The proposed model can take advantage of the inter-relationships between data samples. Our approach leads to a significant reduction of manual labeling cost while achieving similar or better recognition performance compared to a model trained with entire training set. This is demonstrated through rigorous experimentation on five datasets.
Year
DOI
Venue
2017
10.1109/CVPR.2017.89
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Keywords
Field
DocType
information theory,training data,active learning,informative representative selection,computer vision,data compression,sample selection strategy,classification model,annotation cost reduction,manual labeling cost reduction
Data modeling,Data mining,Computer science,Context model,Artificial intelligence,Information theory,Annotation,Activity recognition,Active learning,Pattern recognition,Exploit,Data compression,Machine learning
Conference
Volume
Issue
ISSN
2017
1
1063-6919
ISBN
Citations 
PageRank 
978-1-5386-0458-8
2
0.36
References 
Authors
31
4
Name
Order
Citations
PageRank
Jawadul H. Bappy1755.64
Sujoy Paul2757.66
E. Tuncel314012.78
Amit K. Roy Chowdhury4115373.96