NDLI: Unsupervised feature learning using Markov deep belief network

Content Provider	IEEE Xplore Digital Library
Author	Dongyang Cheng Tanfeng Sun Xinghao Jiang Shilin Wang
Copyright Year	2013
Description	Author affiliation: Sch. of Inf. Security Eng., Shanghai Jiao Tong Univ., Shanghai, China (Dongyang Cheng; Tanfeng Sun; Xinghao Jiang; Shilin Wang)
Abstract	Recently, deep architectures, such as Deep Belief Network (DBN), have been used to learn features from unlabeled data. However, since DBN supports bi-directional inference and the units between two layers are fully connected, it is difficult to directly apply the traditional convolutional network to DBN, or scale DBN to fit the large images (e.g. 1024×768). In this paper, a new deep learning model, named Markov DBN (MDBN), is proposed to address these problems. This model employs a new way for DBN to reduce computational burden and handle large images. Markov sub-layers are also adopted to take the neighboring relationship of the inputs into consideration. To train MDBN, we devise Block Restricted Boltzmann Machine (BRBM) which chooses non-overlapping blocks as input. Furthermore, SIFT descriptor is employed to enable this model to learn translation, scaling and rotation invariant features. Experimental results on datasets Caltech-101 and Caltech-256 have demonstrated the superiority of our model.
Starting Page	260
Ending Page	264
File Size	423958
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738054
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Markov processes Feature extraction Noise measurement Convolutional codes Training Computational modeling Information processing SIFT Deep learning Block RBM Markov DBN image classification
Content Type	Text
Resource Type	Article

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