Strategy of combining random subspace and diversified active learning in CBIR

摘要

Generally speaking, several aspects related to relevance feedback based CBIR include what means should be adopted for approximate semantic description of image content, what strategies be applied to sample labeling in feedback and what relevance model would be built for online discrimination. Using random sampling strategy, we construct a set of random subspaces for learning multiple intrinsic descriptions of image content, with each of which stable component classifier can be trained. To enhance the generalization capability of relevance model, the diversified active learning is carried out by collecting more informative samples, i.e. those samples spreading around decision boundary dispersedly. The final favorable performance also contributes to the application of ensemble scheme on individual component classifier.