Contributed by Thomas Kailath, April 23, 2018 (sent for review February 12, 2018; reviewed by Rama Chellappa, Shree Nayar, and Erik Sudderth)
The Internet contains millions of videos and images of objects, captured contextually, just as they would appear in their natural environments. Such access to large-scale data has injected an urgency to an age-old query: Can one automatically browse these data, as a human would, to construct brain-like and computationally tractable models of the visual world? If successful, many of the scalability and robustness limitations of existing computer vision methodologies, which primarily rely on supervised frameworks requiring manually labeled exemplars, will be removed. As a step toward solving this problem, we leverage widely accepted findings in the fields of cognitive psychology and neurophysiology to develop an unsupervised framework for automated isolation of objects and their efficient detection and localization in new environments.
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.
Author contributions: L.C., S.S., T.K., and V.R. designed research; L.C., S.S., T.K., and V.R. performed research; L.C. and V.R. analyzed data; and L.C., T.K., and V.R. wrote the paper.
Reviewers: R.C., University of Maryland, College Park; S.N., Columbia University; and E.K., University of California, Irvine.
The authors declare no conflict of interest.
Data deposition: The in-house dataset used in the paper is shared publicly at https://www.ee.ucla.edu/wp-content/uploads/ee/cele_images_lite.zip.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1802103115/-/DCSupplemental.
Published under the PNAS license.
