Drawing on the correspondence between spectral clustering, spectral dimensionality reduction, and the connections to the Markov chain theory, we present a novel unified framework for structural analysis of image database using spectral techniques. The framework provides a computationally efficient approach to both clustering and dimensionality reduction, or 2-D visualization. Within this framework, we can also infer the semantic degrees of the images, i.e. imagerank, which characterize the richness of semantics contained in the images. Some illustrative examples are discussed.