Image segmentation, shape detection and object recognition are three tightly coupled and intertwined visual processes. Only by integrating them together into a coherent, unified system can there be hope for achieving the long-standing goal of large-scale visual recognition. This proposal puts forward a learning-based unified graph formulation providing a principled and workable framework for attacking this extremely difficult problem.
The proposed research efforts are directed along three fronts: 1) complete inference of object information by constructing multi-layer graph for shape detection, object recognition and segmentation; 2) unbiased integration of bottom-up low level cues with top-down knowledge of object shape; and 3) direct learning of the object recognition graph with supervised spectral graph cuts learning technique. The proposed 100- and 1000- Object Recognition Challenge will provide a highly objective and rigorous evaluation its success.
This research draws upon ideas from a diverse set of disciplines: computer vision, machine learning, numerical analysis, and theory of computation. PI will continue to promote inter-disciplinary researches through graduate courses, conference tutorials and workshops, internships and research experience for undergraduates, as well as web page resources offering tutorial and open source code.
