Subject region segmentation in disparity maps for image retrieval

The paper presents a method to automatically extract subject regions to be used as a key for image retrieval. If one wants to look up a particular object in an image database, a key image region of the object should be properly indicated to the system. However, this task is not trivial. In our approach, we assume that we have an actual object in the real world to be looked up in the image database. Taking advantage of disparity images using a commercially available stereo range finder, we automate subject region extraction. We assume that 3-D points in a dense disparity map yield a multimodal Gaussian probability distribution, and subject regions are extracted by properly selecting particular modes of Gaussian densities. We demonstrate that the system somehow extracts regions which correspond to "subjects." The system achieves adaptive and quick extraction, and it enables experimental image retrieval by a real object system which runs interactively.