This paper proposes an algorithm named Small Sample Incremental Biased Learning Algorithm to solve three difficulties of relevance feedback in sketch retrieval, including small sample issue, asymmetry of training data and real-time requirement. The algorithm combines active learning, biased classification and incremental learning to model the small sample biased learning problem in relevance feedback process. Active learning employs uncertainty sampling to choose the best labeling samples, so that the generalization ability of classifier is maximized with the limited training data; Biased classification constructs hyperspheres to treat positive and negative data differently, which distinguishes the user’s target class accurately; Newly labeled samples in each feedback loop are used to train the classifier incrementally to reduce the training time. Incremental learning also collects training data to further alleviate the small sample problem. Experimental results show that this algorithm improves the performance of sketch retrieval. And it can be well extended to other retrieval domains like CBIR (content based image retrieval), 3D retrieval, and so on.