Evaluating Three Image Segmentation Algorithms from Two Perspectives: Segmentation Error Measures and Image Annotation

Image segmentation has an essential role in the image annotation process which assigns meaningful words to an image taking into account its content. For this reason it is important to identify which segmentation algorithm is producing better results. This evaluation can be made using segmentation error measures for consistency quantification and by analyzing the results of the annotation process for each segmentation algorithm that is investigated. This paper presents a system used for the evaluation of three image segmentation algorithms: the color set back-projection algorithm, the local variation algorithm, the segmentation algorithm based on a hexagonal structure from two perspectives: segmentation error measures and image annotation.