Article ID Journal Published Year Pages File Type
383430 Expert Systems with Applications 2012 9 Pages PDF
Abstract

In this work, we study the problem of cross-domain video concept detection, where the distributions of the source and target domains are different. Active learning can be used to iteratively refine a source domain classifier by querying labels for a few samples in the target domain, which could reduce the labeling effort. However, traditional active learning method which often uses a discriminative query strategy that queries the most ambiguous samples to the source domain classifier for labeling would fail, when the distribution difference between two domains is too large. In this paper, we tackle this problem by proposing a joint active learning approach which combines a novel generative query strategy and the existing discriminative one. The approach adaptively fits the distribution difference and shows higher robustness than the ones using single strategy. Experimental results on two synthetic datasets and the TRECVID video concept detection task highlight the effectiveness of our joint active learning approach.

► We propose a joint active learning method for cross-domain video concept detection. ► A generative query strategy is proposed when the two distributions differ a lot. ► Experimental results show the robustness and the effectiveness of the joint method.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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