Content-based retrieval of multimedia data has still been an active research area. The efficient retrieval in natural images has been proven a difficult task for content-based image retrieval systems. In this paper, we present a system that adapts two different index structures, namely Slim-Tree and BitMatrix, for efficient retrieval of images based on multidimensional low-level features such as color, texture and shape. These index structures also use metric space. We use MPEG-7 Descriptors extracted from images to represent these features and store them in a native XML database. The low-level features; Color Layout (CL), Dominant Color (DC), Edge Histogram (EH) and Region Shape (RS) are used in Slim-Tree and BitMatrix and aggregated by Ordered Weighted Averaging (OWA) method to find final similarity between any two objects. The experiments included in the paper are in the subject of index construction and update, query response time and retrieval effectiveness using ANMRR performance metric and precision/recall scores. The experimental results strengthen the case that uses BitMatrix along with Ordered Weighted Averaging method in content-based image retrieval systems.