In this paper, we present a new scalable segmentation algorithm called JHMS (Joint Hierarchical and Multiresolution
Segmentation) that is characterized by region-based hierarchy and resolution scalability. Most of the
proposed algorithms either apply a multiresolution segmentation or a hierarchical segmentation. The proposed
approach combines both multiresolution and hierarchical segmentation processes. Indeed, the image is considered
as a set of images at different levels of resolution, where at each level a hierarchical segmentation is performed.
Multiresolution implies that a segmentation of a given level is reused in further segmentation processes operated
at next levels so that to insure contour consistency between different resolutions. Each level of resolution provides
a Region Adjacency Graph (RAG) that describes the neighborhood relationships between regions within
a given level of the multiresolution representation. Region label consistency is preserved thanks to a dedicated
projection algorithm based on inter-level relationships. Moreover, a preprocess based on a quadtree partitioning
reduces the amount of input data thus leading to a lower overall complexity of the segmentation framework.
Experiments show that we obtain effective results when compared to the state of the art together with a lower