The concept of group construction vector and independent construction vector for visual cryptography is proposed, and the method based on construction vector is presented for con-structing basis matrices. The general ...The concept of group construction vector and independent construction vector for visual cryptography is proposed, and the method based on construction vector is presented for con-structing basis matrices. The general solutions to construction vectors and the general solutions to k out of n visual cryptographic schemes are obtained. Using the construction vectors, everyone can construct visual cryptographic schemes simply and efficaciously according to the formulas. The concept and the general solutions to construction vector present a good idea for researches on visual cryptographic schemes, including structural properties, the bound of pixel expansion and contrast, and optimal construction.展开更多
Aiming at the low recognition accuracy of non-negative matrix factorization(NMF)in practical application,an improved spare graph NMF(New-SGNMF)is proposed in this paper.New-SGNMF makes full use of the inherent geometr...Aiming at the low recognition accuracy of non-negative matrix factorization(NMF)in practical application,an improved spare graph NMF(New-SGNMF)is proposed in this paper.New-SGNMF makes full use of the inherent geometric structure of image data to optimize the basis matrix in two steps.A threshold value s was first set to judge the threshold value of the decomposed base matrix to filter the redundant information in the data.Using L2 norm,sparse constraints were then implemented on the basis matrix,and integrated into the objective function to obtain the objective function of New-SGNMF.In addition,the derivation process of the algorithm and the convergence analysis of the algorithm were given.The experimental results on COIL20,PIE-pose09 and YaleB database show that compared with K-means,PCA,NMF and other algorithms,the proposed algorithm has higher accuracy and normalized mutual information.展开更多
文摘The concept of group construction vector and independent construction vector for visual cryptography is proposed, and the method based on construction vector is presented for con-structing basis matrices. The general solutions to construction vectors and the general solutions to k out of n visual cryptographic schemes are obtained. Using the construction vectors, everyone can construct visual cryptographic schemes simply and efficaciously according to the formulas. The concept and the general solutions to construction vector present a good idea for researches on visual cryptographic schemes, including structural properties, the bound of pixel expansion and contrast, and optimal construction.
基金This work was supported by the National Natural Science Foundation of China(Grant No.61501005)the Anhui Natural Science Foundation(Grant No.1608085 MF 147)+2 种基金the Natural Science Foundation of Anhui Universities(Grant No.KJ2016A057)the Industry Collaborative Innovation Fund of Anhui Polytechnic University and Jiujiang District(Grant No.2021cyxtb4)the Science Research Project of Anhui Polytechnic University(Grant No.Xjky2020120).
文摘Aiming at the low recognition accuracy of non-negative matrix factorization(NMF)in practical application,an improved spare graph NMF(New-SGNMF)is proposed in this paper.New-SGNMF makes full use of the inherent geometric structure of image data to optimize the basis matrix in two steps.A threshold value s was first set to judge the threshold value of the decomposed base matrix to filter the redundant information in the data.Using L2 norm,sparse constraints were then implemented on the basis matrix,and integrated into the objective function to obtain the objective function of New-SGNMF.In addition,the derivation process of the algorithm and the convergence analysis of the algorithm were given.The experimental results on COIL20,PIE-pose09 and YaleB database show that compared with K-means,PCA,NMF and other algorithms,the proposed algorithm has higher accuracy and normalized mutual information.
