A promising approach to content-based image retrieval, proposed by Choubey and Raghavan, involves the representation of the original image space, in terms of low-level image features into a feature space, where images are represented as vectors of high-level features. The retrieval system based on that approach consists of three phases: database population, online addition and image retrieval. Though their framework supports content-based retrieval of images, the issues that arise when database grows dynamically and user queries are different from those in database, were not investigated. In the current work, we (i) experimentally investigate issues relating to online addition of new images and image retrieval, and (ii) provide a theoretical analysis of the complexity and effectiveness of our retrieval system by comparing it with the conventional approach. We have performed an extensive set of experiments to test the efficiency, effectiveness and scalability of our approach. The experimental results show that our approach is not only efficient but also effective in retrieving images even when the image database is dynamic and user queries are framed with images that are external to the database.