In the context of computer-assisted plant identification we are facing challenging information retrieval problems because of the very high within-class variability and of the limited number of training examples. To address these problems, we suggest a new interactive learning approach that combines similarity-based retrieval and re-ranking by SVM using local feature distributions. This approach leads to improved sample selection, allowing to obtain better results.