Improving Image Segmentation for Boosting Image Annotation with Irregular Pyramids

Image Segmentation and Automatic Image Annotation are two research fields usually addressed independently. Treating these problems simultaneously and taking advantage of each other’s information may improve their individual results. In this work our ultimate goal is image annotation, which we perform using the hierarchical structure of irregular pyramids. We propose a new criterion to create new segmentation levels in the pyramid using low-level cues and semantic information coming from the annotation step. Later, we use the improved segmentation to obtain better annotation results in an iterative way across the hierarchy. We perform experiments in a subset of the Corel dataset, showing the relevance of combining both processes to improve the results of the final annotation.