This dissertation research will explore region-based and interest points based image representations, two of the most-used image models for object detection, image classification, and visual search among other applications. We will analyze the relationship between both representations with the goal of proposing a new hybrid representation that takes
advantage of the strengths and overcomes 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.