A framework for the efficient segmentation of large-format color images

A novel approach to large-format image segmentation is presented, focused on usage in content-based multimedia applications. The proposed framework aims at facilitating the time-efficient segmentation of large-format images while maintaining the high perceptual quality of the segmentation result. For this to be achieved, the employed segmentation algorithm is applied to reduced versions of the large-format images, in order to speed-up its execution, resulting in a coarse-grained segmentation mask. The final fine-grained segmentation mask is produced by an enhancement stage that involves partial reclassification of the pixels of the original image using a Bayes classifier. As shown by experimental evaluation, this novel scheme provides fast segmentation with high perceptual segmentation quality.