Still Image Segmentation Tools For Object-Based Multimedia Applications

In this paper, a color image segmentation al- gorithm and an approach to large-format image segmen- tation are presented, both focused on breaking down im- ages to semantic objects for object-based multimedia ap- plications. The proposed color image segmentation algo- rithm performs the segmentation in the combined intensity- texture-position feature space in order to produce con- nected regions that correspond to the real-life objects shown in the image. A preprocessing stage of conditional image fil- tering and a modified K-Means-with-connectivity-constraint pixel classification algorithm are used to allow for seamless integration of the different pixel features. Unsupervised op- eration of the segmentation algorithm is enabled by means of an initial clustering procedure. The large-format image seg- mentation scheme employs the aforementioned segmenta- tion algorithm, providing an elegant framework for the fast segmentation of relatively large images. In this framework, the segmentation algorithm is applied to reduced versions of the original images, in order to speed-up the completion of the segmentation, resulting in a coarse-grained segmen- tation mask. The final fine-grained segmentation mask is produced with partial reclassification of the pixels of the original image to the already formed regions, using a Bayes classifier. As shown by experimental evaluation, this novel scheme provides fast segmentation with high perceptual seg- mentation quality.