NDLI: Stacks of convolutional Restricted Boltzmann Machines for shift-invariant feature learning

Content Provider	IEEE Xplore Digital Library
Author	Norouzi, M. Ranjbar, M. Mori, G.
Copyright Year	2009
Description	Author affiliation: Sch. of Comput. Sci., Simon Fraser Univ., Burnaby, BC, Canada (Norouzi, M.; Ranjbar, M.; Mori, G.)
Abstract	In this paper we present a method for learning class-specific features for recognition. Recently a greedy layer-wise procedure was proposed to initialize weights of deep belief networks, by viewing each layer as a separate restricted Boltzmann machine (RBM). We develop the convolutional RBM (C-RBM), a variant of the RBM model in which weights are shared to respect the spatial structure of images. This framework learns a set of features that can generate the images of a specific object class. Our feature extraction model is a four layer hierarchy of alternating filtering and maximum subsampling. We learn feature parameters of the first and third layers viewing them as separate C-RBMs. The outputs of our feature extraction hierarchy are then fed as input to a discriminative classifier. It is experimentally demonstrated that the extracted features are effective for object detection, using them to obtain performance comparable to the state of the art on handwritten digit recognition and pedestrian detection.
Starting Page	2735
Ending Page	2742
File Size	476959
Page Count	8
File Format	PDF
ISBN	9781424439928
ISSN	10636919
DOI	10.1109/CVPR.2009.5206577
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning Feature extraction Computer vision Cellular neural networks Object detection Object recognition Neural networks Nonhomogeneous media Filters Large-scale systems
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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