Learning to Recognize Objects in Images Using Anisotropic Nonparametric Kernels

Authors:
Douglas Summers-Stay;Yiannis Aloimonos
Affiliations:
University of Maryland, College Park;University of Maryland, College Park
Venue:
Proceedings of the 2010 conference on Biologically Inspired Cognitive Architectures 2010: Proceedings of the First Annual Meeting of the BICA Society
Year:
2010

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Abstract

We present a system that makes use of image context to perform pixel-level segmentation for many object classes simultaneously. The system finds approximate nearest neighbors from the training set for a (biologically plausible) feature patch surrounding each pixel. It then uses locally adaptive anisotropic Gaussian kernels to find the shape of the class manifolds embedded in the high-dimensional space of the feature patches, in order to find the most likely label for the pixel. An iterative technique allows the system to make use of scene context information to refine its classification. Like humans, the system is able to quickly make use of new information without going through a lengthy training phase. The system provides insight into a possible mechanism for infants to quickly learn to recognize all of the classes they are presented with simultaneously, rather than having to be trained explicitly on a few classes like standard image classification algorithms.