This project addresses the problem of category-level object recognition in images: Its aim is to develop effective methodologies for representing object classes; learning the corresponding object models from cluttered sample images in a semi-supervised manner; and efficiently and robustly recognizing instances of these models in novel images despite clutter, occlusion, viewpoint and illumination changes, and individual variations within each class.
Intellectual Merit. The scientific objective of this project is to develop a representation of the salient parts of an object and their relationships that can effectively be learned from heavily cluttered data in a weakly supervised way, correctly captures within-class variability and appearance changes due to variations in viewpoint and illumination, and effectively supports inference over object models and the automated construction of efficient classification machines.
Broader Impacts. This project will investigate applications of category-level object recognition to image retrieval, video annotation, human-computer interaction; surveillance and security; and robotics via international academic and industrial collaborations. Contributions to education and outreach will include training PhD students and post-doctoral researchers, and involving underrepresented groups in graduate research and undergraduate data collection and empirical evaluation projects.
