Online feature selection based on generalized feature contrast model

To really bridge the gap between high-level semantics and low-level features in content-based image retrieval (CBIR), a problem that must be solved is which features are suitable for explaining the current query concept. We propose a novel feature selection (FS) criterion based on a psychological similarity measurement, generalized feature contrast model, and implement an online feature selection algorithm in a boosting manner to select the most representative features and do classification during each feedback round. The advantages of the proposed method are: it does not require a Gaussian assumption for "relevant" images as other online FS methods do; it accounts for the intrinsic asymmetry between "relevant" and "irrelevant" image sets in CBIR online learning; it is very fast. Extensive experiments have shown our algorithm\´s effectiveness.