Image indexing and retrieval using a concept-based approach involves extraction, modelling and indexing of image content information. Computer vision offers a variety of techniques for searching images in large collections. We propose a method that enables components of an image to be categorised on the basis of their relative importance in combination with filtered representations. Our method concentrates on matching subparts of images, defined in a variety of ways, in order to find particular objects. These ideas are illustrated with a variety of examples. We focus on Concept-based Image Indexing and Retrieval (CIIR), using a fuzzy expert systems, density measure, supporting factors and other attributes of image components to identify and retrieve images accurately and efficiently.