Texture images have statistical properties, structural properties, or both. They may not only consist of the structured and/or random placement of elements, but also may be without fundamental subunits. Moreover, due to the diversity of textures appearing in natural images it is difficult to define texture precisely. Texture is an important element to human vision and has been found to provide cues to scene depth and surface orientation. This paper presents new methods for integrating spatial and feature information in order to improve systems for image retrieval, analysis, and compression. In particular, this paper develops and demonstrates an integrated feature for image retrieval, a general framework for extracting spatially localized features from images using histogram, a system for image compression, and a representation of texture based upon spatial-frequency energy histograms. The visually important information within images is often confined to spatially localized regions, or is represented by the spatial arrangements of these regions. By developing the processes that analyze and represent images in this way, we have improved our capabilities to develop powerful content-based image retrieval and image compression systems.