This dissertion research explores two of the most-used image models for object detection, 3D reconstruction, visual search: region-based and interest-points image representations; and will try to provide a new image model to take advantage of the strengths and overcome the weaknesses of both approaches. More specifically, we will focus on the gPb-owt-ucm segmentation algorithm and the SIFT local features since they are the most contrasted techniques in their respective fields. Furthermore, using an object retrieval benchmark, this dissertation research will analyze three basic questions: (i) the usefulness of an interest points hierarchy based on a contour strength signal, (ii) the influence of the context on both interest points location and description, and (iii) the analysis of regions as spatial support for bundling interest points.