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Canonical correlation analysis networks for two-view image recognition.pdf
Information Sciences 385–386 (2017) 338–352
Contents lists available at ScienceDirect
Information Sciences
journal homepage: /locate/ins
Canonical correlation analysis networks for two-view image recognition
Xinghao Yang a, Weifeng Liu a,?, Dapeng Tao b,?, Jun Cheng c,d
a College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China b bSchool of Information Science and Engineering, Yunnan University, Kunming 650091, Yunnan, China c Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China d The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China
article info
Article history: Received 2 August 2016 Revised 14 December 2016 Accepted 2 January 2017 Available online 4 January 2017
Keywords: Canonical correlation analysis Deep learning Convolutional neural networks Image classi?cation
abstract
In recent years, deep learning has attracted an increasing amount of attention in machine learning and arti?cial intelligence areas. Currently, many deep learning network-related architectures such as deep neural networks (DNNs), convolutional neural network (CNN), wavelet scattering network (ScatNet) and principal component analysis network (PCANet) have been proposed. The most effective network is PCANet, which has achieved promising performance in image classi?cation, such as for face, object and handwritten digit recognition. PCANet can only handle data that are represented by single-view features. In this paper, we present a canonical correlation analysis network (CCANet) to address image classi?cation, in which images are represented by two-view features. The CCANet learns two-view multistage ?lter banks by a canonical correlation analysis (CCA) method and constructs a cascaded convolutional deep network. Then, we incorporate ?lters with binaryzation and block-wise histogram processes to form the ?nal depth structure. In addition, we introduce a variation of CCANe
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