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Feng Q.,Harbin Institute of Technology | Zhu X.,China Hua Rong Holdings Corporation LTD | Pan J.-S.,Shenzhen University
Optik | Year: 2015

In this paper, a novel classifier based on linear regression classification (LRC), called global linear regression coefficient (GLRC) classifier, is proposed for recognition. LRC classifier uses the test sample and the class subspace to calculate the distance which will be used for classification. GLRC classifier uses the test sample vector and whole train space (all the class subspaces) to calculate the global linear regression coefficient. Then GLRC computes the signed square sum of the linear regression coefficients belonging to the same class, and the result will be used for classification. A large number of experiments on Yale face database and AR face database are used to evaluate the proposed algorithm. The experimental results demonstrate that the proposed method achieves better recognition rate than LRC classifier, sparse representation based classification (SRC) classifier, Collaborative representation based classification (CRC) classifier and two phase test sample sparse representation (TPTSSR) classifier and so on. © 2015 Elsevier GmbH. Source


Feng Q.,Harbin Institute of Technology | Zhu X.,China Hua Rong Holdings Corporation LTD | Pan J.-S.,Harbin Institute of Technology
Optik | Year: 2014

In this paper, a novel classifier based on two-phase test sample sparse representation (TPTSSR) classifier and coarse k nearest neighbor (C-kNN) classifier, called novel classification rule of two-phase test sample sparse representation (NCR-TPTSSR) classifier, is proposed for image recognition. Being similar to TPTSSR classifier and C-kNN classifier, NCR-TPTSSR classifier also uses the two phases to classify the test sample. However, the classification rule of NCR-TPTSSR classifier is different to the decision rule of TPTSSR classifier and C-kNN classifier. A large number of experiments on FERET face database, AR face database, JAFFE face database and PolyU FKP database are used to evaluate the proposed algorithm. The experimental results demonstrate that the proposed method achieves better recognition rate than TPTSSR classifier, C-kNN classifier, nearest feature center (NFC) classifier, nearest feature line (NFL) classifier, nearest neighbor (NN) and so on. © 2014 Elsevier GmbH. All rights reserved. Source


Zhu X.,China Hua Rong Holdings Corporation LTD | Xu Y.,Harbin Institute of Technology | Xu Y.,A+ Network | Chen H.,China Hua Rong Holdings Corporation LTD | And 3 more authors.
Optik | Year: 2013

This paper proposes an efficient fire detection method for intelligent monitoring of self-service banks. This method includes the following steps: motion region detection, color-based fire detection and fire region refinement. These steps are as follows. First, the method uses four Gaussian distributions to construct an adaptive background model of the scene, and exploits this model to obtain the moving regions of the image. Second, the method uses RGB color and HSI color to determine whether the moving regions are fire candidates or not. These two steps are able to detect almost all of the true fire regions but it also takes a portion of non-fire regions as the fire. Third, the proposed frame difference procedure and the distance of two fire regions of two adjacent frames to eliminate the false fire regions obtained using the first two steps. The experimental results show that the proposed method performs very well in small-scale fire detection. The false reject rate (FRR) and false accept rate (FAR) of our method are 10.4%, and 6.02%, respectively. © 2013 Elsevier GmbH. All rights reserved. Source

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