Research On Canonical Correlation Analysis Algorithms For Multi-view Image Recognition

With the development of portable digital image collection and storage equipment,a vast of digital images are available in our daily life.These images,on one hand,make our life more intelligent;and on the other hand,bring new challenges for machine learning and image recognition fields.There are usually two problems in practical applications:(1)no labeled image samples on hand to train the classification model;(2)many multi-view images are not sufficiently used.Multi-view learning can take advantage of the complementary information in multi-view images,and learn more about the essential features of the target object,thus improving the classification performance.Canonical correlation analysis(CCA)algorithm is a kind of unsupervised multi-view learning algorithm,which is a good way to solve the above problems.At present,the CCA and its improved algorithms have been widely used in data mining and pattern recognition fields.Based on the in-depth study of the CCA algorithm,the main contribution of this paper are as follows:1.A Hessian regularized multiset canonical correlation analysis method(Hes MCC)is proposed.Compared with Laplacian regularization,Hessian regularization can better preserve the local geometry structure of the data manifold.We conduct experiments on USPS handwritten digits database,Yale-B and Choke Point face recognition database and ETH-80 object recognition database.Experimental results illustrate that Hes MCC is superior to traditional multi-view canonical correlation analysis(MCCA)and Laplacian regularized MCCA(Lap MCC).2.A deep CCA network(CCANet)is proposed.In CCANet,two view filter banks are calculated by CCA,enabling the extracted feature contains both deep hierarchical feature and multi-view feature.Compared with the single-view principle component analysis(PCANet),CCANet achieves a higher recognition accuracy in terms of object recognition,face recognition and handwritten digits recognition.3.A tensor canonical correlation analysis network(TCCANet)is proposed.In TCCANet,the tensor canonical correlation analysis(TCCA)is utilized to compute arbitrary number views of filter banks,which breaks the two-view limitation in CCANet.Experimental results shows that TCCANet outperforms previous PCANet,CCANet,MS-CCANet and MCCANet for multi-view remote sensing(RS)scene recognition.