Content-based image retrieval (CBIR) with global features is notoriously noisy, especially for image queries with low percentages of relevant images in a collection. Moreover, CBIR typically ranks the whole collection, which is inefficient for large databases. The objective of a general-purpose content-based image retrieval system is to find images in a database that match an external measure of relevance. Since users follow different and inconsistent relevance measures, processing queries in a task-specific manner has shown to be an effective approach. In content-based image retrieval (CBIR), images are represented by global or local features. Global features are capable of generalizing an entire image with a single vector, describing color, texture, or shape. Local features are computed at multiple points on an image and are capable of recognizing objects. CBIR with global features is notoriously noisy for image queries of low generality, i.e. the fraction of relevant images in a collection. In contrast to text retrieval where documents matching no query keyword are not retrieved, CBIR methods typically rank the whole collection via some distance measure. In this project, the system proposes a compressed-domain image filtering and re-ranking approach for multi-agent image retrieval.