NDLI: Learning features and their transformations from natural videos

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Learning features and their transformations from natural videos

Content Provider	IEEE Xplore Digital Library
Author	Dutta, J.K. Banerjee, B.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Memphis, Memphis, TN, USA (Dutta, J.K.; Banerjee, B.)
Abstract	Learning features invariant to arbitrary transformations in the data is a requirement for any recognition system, biological or artificial. It is now widely accepted that simple cells in the primary visual cortex respond to features while the complex cells respond to features invariant to different transformations. We present a novel two-layered feedforward neural model that learns features in the first layer by spatial spherical clustering and invariance to transformations in the second layer by temporal spherical clustering. Learning occurs in an online and unsupervised manner following the Hebbian rule. When exposed to natural videos acquired by a camera mounted on a cat's head, the first and second layer neurons in our model develop simple and complex cell-like receptive field properties. The model can predict by learning lateral connections among the first layer neurons. A topographic map to their spatial features emerges by exponentially decaying the flow of activation with distance from one neuron to another in the first layer that fire in close temporal proximity, thereby minimizing the pooling length in an online manner simultaneously with feature learning.
Starting Page	55
Ending Page	61
File Size	1029523
Page Count	7
File Format	PDF
ISBN	9781479945153
DOI	10.1109/CIDUE.2014.7007867
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radio frequency Computational modeling Biological system modeling Neurons Predictive models Feedforward neural networks Videos
Content Type	Text
Resource Type	Article

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