In this paper, we aim to get to the content-based retrieval of nonuniformly textured objects from natural scenes under varying illumination and viewing conditions. Nonuniformly textured objects are objects containing irregular texture elements such as trees, animals (e.g. lions), walls, and grass. To cope with irregular texture contents, the texture measure is based on comparing feature distributions based on the multidimensional histogram intersection of color ratio derivatives. It is shown that color ratio derivatives are robust to a change in illumination, camera viewpoint, and pose of the textured object. Color ratio derivatives are computed from the RGB color channels of a ccd color camera as well as from spectral data obtained by a spectrograph. To cope with object cluttering, a region-based texture segmentation is applied on the target images in the image database prior to the actual image retrieval process. The region-based segmentation algorithm computes regions or blobs having roughly the same texture content as the query image. After segmenting the target images into blobs, the retrieval process is based on computing the histogram intersection of color ratio derivatives derived from query image and target blobs. Experiments have been conducted on images taken from colored, textured objects. Different light sources have been used to illuminate the objects in the scene. From the theoretical and experimental results, it is concluded that color constant texture matching in image libraries provides high retrieval accuracy and is robust to varying illumination and viewing conditions.