Semantic object segmentation by dynamic learning from multiple examples

Abstract

We present a novel "dynamic learning" approach for an intelligent image database system to automatically improve object segmentation and labeling without user intervention, as new examples become available, for object-based indexing. The proposed approach is an extension of our earlier work on "learning by example," which addressed labeling of similar objects in a set of database images based on a single example. It utilizes multiple example object templates to improve the accuracy of existing object segmentations and labels. We also propose to use Normalized Area of Symmetric Differences (NASD) as the similarity metric in "dynamic learning", due to its robustness to boundary noise that results from automatic image segmentation. The performance of the dynamic learning concept is demonstrated by experimental results.