Learning visual object categories, and recognizing objects in images, is perhaps the most difficult and exciting problem in machine vision today. In light of the fast growing data deluge in science, engineering, industry and society, recognition systems must be able to operate without human supervision. This poses new challenges: How can one learn automatically models of a large number of object classes from unlabelled images? How can one represent these object classes such that they can be searched efficiently? How can one leverage the learnt models to learn new object classes from very few examples?
It is proposed that these challenges may be met by inferring hierarchical representations of object classes from unlabelled image data. Object classes are represented as constellations of parts, where each part extracts shape and appearance information. Non-parametric Bayesian techniques may be employed to organize these object classes into tree-structured representations. The richness of this representation grows incrementally as more data is presented to the system. New similarity measures between object classes naturally derive from this representation facilitating recognition.
Outreach to the local community is established through a collaboration with the California State University Northridge where students, often minorities who are the first in the family to obtain a university degree, will have the opportunity to engage in visual recognition problems proposed by and relevant to local companies.
