CAREER: Learning to See - A Unified Segmentation and Recognition Approach

University Of Pennsylvania, Philadelphia PA

Investigators

Abstract

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.