In this paper, we propose an active and inductive combined learning method to learn users' image query concepts. We model query concepts in K-CNF, which can be used to express most practical queries. To learn a user's query concept, we propose MEGA. MEGA initializes a user's query concept as the conjunction of all disjunctions of at most length K of the predicates. It then intelligently selects unlabeled data to present to the user for gathering information to eliminate the maximum expected number of disjunctions. MEGA maximizes the usefulness of each example it generates for learning a user's query concept and hence expedites the convergence to the target concept. Through analysis and experiments, we show that MEGA can learn a complex image query concept much faster than some traditional schemes.