Region-based semi-supervised clustering image segmentation

To make image segmentation accord with user´s inclinations, semi-supervised clustering image segmentation is used. It applies manual guides and pays more attention to user´s preferences. Watershed is adopted to segment image into series of small regions, which are basic units for segmentation. In the method, adjacent or nearby regions for labeled regions are assumed to belong to the same cluster. Labeled data and unlabeled data are gotten based on manual guides and assigned different weights during iterative processes. A penalty function is introduced when labeled data are incorrectly segmented. For a complex object to be segmented, its different parts are first segmented independently, and the outputs are merged finally. The experimental results show that region-based semi-supervised clustering image segmentation is fast and precise, and its classification results are more in line with user´s requirements.