Existing video research incorporates the use of relevance feedback based on user-dependent interpretations to improve the retrieval results. In this paper, we segregate the process of relevance feedback into 2 distinct facets: (a) recall-directed feedback; and (b) precision-directed feedback. The recall-directed facet employs general features such as text and high level features (HLFs) to maximize efficiency and recall during feedback, making it very suitable for large corpuses. The precision-directed facet on the other hand uses many other multimodal features in an active learning environment for improved accuracy. Combined with a performance-based adaptive sampling strategy, this process continuously re-ranks a subset of instances as the user annotates. Experiments done using TRECVID 2006 dataset show that our approach is efficient and effective.