Relevance feedback and semantic retrieval have received extensive attention recently in the computer vision community. In this article, we present a semantic image query system with integrated feedback mechanism. Our system has two major components: the low-level feature space and the semantic space. In the low-level feature space, images are described by multidimensional vectors and are clustered based on the similarity of their contents. In the semantic space, the relationship among keywords is captured by a semantic hierarchy built by the aid of WordNet. Based on our system architecture, we propose a novel feedback solution for semantic retrieval called semantic feedback, which allows our system to interact with users directly at the semantic level. The short-term and long-term learning process of the semantic feedback substantially improves the image retrieval and annotation performance of our system. We demonstrate the effectiveness of our approach with experiments using 5,000 images from Corel database. The significant contribution of this article is in the scenario of having a relatively small training data set compared to the testing data set. Some of the previous work in the same direction has chosen a very large training set and a very small testing set. Clearly, the problem that we try to solve is more realistic and challenging.