The proliferation of content-based image retrieval techniques has highlighted the need to understand the relationship between image clustering based on low-level image features and image clustering made by human users. In conventional image retrieval systems, a range of features such as color, texture, and shape typically characterizes images. However, little is known to what extent these low-level features can be effectively combined with information visualization techniques such that users may explore images in a digital library according to visual similarities. In this article, we compared and analyzed a number of Pathfinder networks of images generated based on such features. Salient structures of images are visualized according to features extracted from color, texture, and shape orientation. Implications for visualizing and constructing hypermedia systems are discussed.