Active learning using the data distribution for interactive image classification and retrieval

In the context of image search and classification, we describe an active learning strategy that relies on the intrinsic data distribution modeled as a mixture of Gaussians to speed up the learning of the target class using an interactive relevance feedback process. The contributions of our work are twofold: First, we introduce a new form of a semi-supervised C-SVM algorithm that exploits the intrinsic data distribution by working directly on equiprobable envelopes of Gaussian mixture components. Second, we introduce an active learning strategy which allows to interactively adjust the equiprobable envelopes in a small number of feedback steps. The proposed method allows the exploitation of the information contained in the unlabeled data and does not suffer from the drawbacks inherent to semi-supervised methods, e.g. computation time and memory requirements. Tests performed on a database of high-resolution satellite images and on a database of color images show that our system compares favorably, in terms of learning speed and ability to manage large volumes of data, to the classic approach using SVM active learning.