  Despite significant recent progress, machine vision systems lag considerably
behind their biological counterparts in performance, scalability, and
robustness. A distinctive hallmark of the brain is its ability to automatically
discover and model objects, at multiscale resolutions, from repeated exposures
to unlabeled contextual data and then to be able to robustly detect the learned
objects under various nonideal circumstances, such as partial occlusion and
different view angles. Replication of such capabilities in a machine would
require three key ingredients: (i) access to large-scale perceptual data of the
kind that humans experience, (ii) flexible representations of objects, and
(iii) an efficient unsupervised learning algorithm. The Internet fortunately
provides unprecedented access to vast amounts of visual data. This paper
leverages the availability of such data to develop a scalable framework for
unsupervised learning of object prototypes--brain-inspired flexible, scale, and
shift invariant representations of deformable objects (e.g., humans,
motorcycles, cars, airplanes) comprised of parts, their different
configurations and views, and their spatial relationships. Computationally, the
object prototypes are represented as geometric associative networks using
probabilistic constructs such as Markov random fields. We apply our framework
to various datasets and show that our approach is computationally scalable and
can construct accurate and operational part-aware object models much more
efficiently than in much of the recent computer vision literature. We also
present efficient algorithms for detection and localization in new scenes of
objects and their partial views.
