Fast pseudo-semantic segmentation for joint region-based hierarchical and multiresolution representation

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 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 a further segmentation process operated at the next level so that to insure contour consistency between different resolutions. Each level of resolutions 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 low complexity.