Entropy-based active learning with support vector machines for content-based image retrieval

An entropy-based active learning scheme with support vector machines (SVMs) is proposed for relevance feedback in content-based image retrieval. The main issue in active learning for image retrieval is how to choose images for the user to label in the next interaction. According to information theory, we proposed an entropy-based criterion for good request selection. To apply the criterion with SVMs, probabilistic outputs are required. Since standard SVMs do not provide such outputs, two techniques are used to produce probabilities. One is to train the parameters of an additional sigmoid function. The other is to use the notion of version space. Experimental results on a database of 10,000 general-purpose images demonstrate the effectiveness of the proposed active learning scheme.