Content-based image retrieval (CBIR) is playing a key role in digital image/video library management and multimedia information processing. It is one of the focal research areas in the proposed MPEG-7 Standard for multimedia communications. Its applications range from telemedicine to distance education, entertainment industry and many more. Although many methods have been proposed and implemented in retrieval systems, the state-of-the-art is far from convenient use in the commercial world. Among other issues, two longstanding problems must be solved: a) gaps between the high level concepts and the low level features; b) the subjectivity of human perception. This is particularly true with the current compressed domain video/image coding standards (e.g. DCT in JPEG, MPEG-1 and 2, Wavelets in JPEG2000). Due to the apparent gaps between the features in the compressed domain (DCT and WT/VQ coefficients) and human perceptin, and the linear comparison criteria used, performance of the current retrieval systems is far from satisfactory. In this talk, I will present our recent work on interactive CBIR. In particular, we propose a retrieval system with several novel query models and nonlinear search units to bridge the gaps between high level concepts and low level features, and to simulate human perception. The proposed method has been tested on images from numerous digital image libraries. Comparison with the well know MARS system shows that our method consistently provides superior retrieval performance.