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Multi-Class Active Learning for Image Classification

9 years 10 months ago
Multi-Class Active Learning for Image Classification
One of the principal bottlenecks in applying learning techniques to classification problems is the large amount of labeled training data required. Especially for images and video, providing training data is very expensive in terms of human time and effort. In this paper we propose an active learning approach to tackle the problem. Instead of passively accepting random training examples, the active learning algorithm iteratively selects unlabeled examples for the user to label, so that human effort is focused on labeling the most “useful” examples. Our method relies on the idea of uncertainty sampling, in which the algorithm selects unlabeled examples that it finds hardest to classify. Specifically, we propose an uncertainty measure that generalizes margin-based uncertainty to the multi-class case and is easy to compute, so that active learning can handle a large number of classes and large data sizes efficiently. We demonstrate results for letter and digit recognition on datasets f...
Ajay J. Joshi, Fatih Porikli, Nikolaos Papanikolop
Added 14 May 2009
Updated 16 Jun 2009
Type Conference
Year 2009
Where IEEE CVPR
Authors Ajay J. Joshi, Fatih Porikli, Nikolaos Papanikolopoulos
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