For the virtual network embedding in Software Defined Network (SDN), the existing researchers mainly consider the acceptance rate, but ignore the problem of the underlying resource failure in SDN. Aiming at the problem of Survivable Virtual Network Embedding (SVNE) in SDN, a virtual network embedding guarantee mechanism was proposed combining priori protection and posteriori recovery. Firstly, the regional resources of SDN physical network were perceived before a virtual request was accepted. Secondly, the backup physical resources for the virtual network elements with relatively reduced remaining resources in the mapping domain were reserved by the priori protection mechanism, and the extended virtual network was embedded into the physical network by the D-ViNE (Deterministic Virtual Network Embedding) algorithm. Finally, when a network element without reserved backup resources was out of order, the fault was recovered by the posterior recovery algorithm, which means the node and the link were recovered by remapping and rerouting respectively. Experimental results show that compared with the SDN-Survivability Virtual Network Embedding algorithm (SDN-SVNE), the proposed mechanism has the virtual request acceptance rate increased by 21.9%. And this protection mechanism has advantages in terms such as virtual level fault recovery rate and physical level fault recovery rate.

Because the performance of the image retrieval system could be effectively improved by using the complementary features, a retrieval method of the texture image using L1 energy and generalized Gaussian distribution parameter features was proposed in the improved Contourlet transform domain. Firstly, the directional subband coefficients went through generalized Gaussian modeling with an improved approach. Then, the texture images were respectively retrieved based on the single feature and the corresponding similarity measurement. Lastly, using the complementary features and the direct summation of their similarity measurements, the texture images were retrieved. The experimental results show that, compared with single feature, the average retrieval rates of the texture image database are effectively improved by the complementary features that fully represent the structural information and the random distribution information.

Locality Preserving Projection (LPP) is a manifold learning method, while the face recognition application of LPP is known to suffer from singular value problem, so a solution scheme using Singular Value Decomposition (SVD) was proposed for recognition application. In this model, the sample data were projected on a non-singular orthogonal matrix to solve the problem of singular value. Then the data of the low dimensional sample space projection subspace were obtained according to the LPP method. The training samples and testing samples were projected onto low-dimensional subspace respectively. Finally the nearest neighbor classifier was used for classification. A series of experiments to compare the proposed algorithm with the traditional local projection algorithm and Principal Component Analysis (PCA) were given on ORL face database. The experimental results demonstrate the efficacy of the improved LPP approach for face recognition.