In this paper we present a method for learning classspecific features for recognition. Recently a greedy layerwise 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. ©2009 IEEE.
CITATION STYLE
Norouzi, M., Ranjbar, M., & Mori, G. (2009). Stacks of convolutional restricted boltzmann machines for shift-invariant feature learning. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009 (pp. 2735–2742). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2009.5206577
Mendeley helps you to discover research relevant for your work.