Relevance feedback: perceptual learning and retrieval in bio-computing, photos, and video.

ABSTRACTOne of the most important characteristics about relevance feedback is that it ideally finds a set of human perceptually correlated results because the user is directly involved in the search process. In principle, relevance feedback is an iterative learning process where positive and negative examples accumulate as the user gives feedback on each new iteration of results. If we view relevance feedback as a learning problem then we can immediately grasp that there will be the associated problem of learning from a small training set. Towards a solution, we present MediaNet, which is an approach toward integrating additional knowledge sources into the relevance feedback process. The additional knowledge sources are used to shape the learning space when insufficient training samples are available. We also integrate genetic or evolutionary algorithms directly into the search process. Experiments are given on test collections in bio-computing, general photos and video