The algorithm of extracting pedestrian features based on texture information has the problems of redundant feature information and being unable to depict the human visual sensitivity, an algorithm named SF-LBP was proposed to extract pedestrian texture feature by Significant Local Binary Pattern which fuses the characteristics of human visual pedestrian system. Firstly, the algorithm calculated the significant factor in each region by saliency detection method. Then, it rebuilt the eigenvector of the image by significant factor weight and pedestrian texture feature, and generated the feature histogram according to local feature. Finally it integrated adaptive AdaBoost classifier to construct pedestrian detection system. The experimental results on INRIA database show that the SF-LBP feature achieves a detection rate of 97% and about 2%-3% higher than HOG (Histogram of Oriented Gradients) feature and Haar feature. It reaches recall rate of 90% and 2% higher than other features. It indicates that the SF-LBP feature can effectively describe the texture characteristics of pedestrians, and improve the accuracy of the pedestrian detection system.

Concerning the low efficiency of network transmission caused by redundant traffic, an algorithm named Packet Feature based Redundancy Traffic Elimination (PFRTE) was proposed based on the protocol-independent traffic redundancy elimination technique. Based on the grouping of packet size, PFRTE dynamically analyzed statistical bimodal characteristics and packet features of network traffic and regarded the size of the packet with the greatest capability of redundancy elimination as the threshold. It decided the boundary points by using sliding window method and calculated the fingerprint of block data within two boundary points. PFRTE encoded the redundant blocks in a simple way and transfered the encoded data instead of redundant data. The experimental results show that, compared with redundant traffic elimination algorithm based on maximum selection and static lookup table selection, PFRTE has the advantage of analyzing the redundancy statistics of network traffic dynamically, and the CPU consumption reduces both at server and client. Meanwhile, the algorithm is also effective with rate of redundancy elimination bytes saving of 8%-40%.

Researches on the audio classification have involved various types of vector features. However, multi-semantics of audio information not only have their own properties, but also have some correlations among them. Whereas, to a certain extent, the simple vector representation cannot represent the multi-semantics and ignore their relations. Tensor Uniform Content Locator (TUCL) was brought forward to express the semantic information of audio, and a three-order Tensor Semantic Space (TSS) was constructed according to the semantic tensor. Tensor Semantic Dispersion (TSD) can aggregate some audio resources with the same semantics, and at the same time, the automatic audio classification can be accomplished by calculating their TSD. And Radical Basis Function Tensor Neural Network (RBFTNN) was constructed and used to train intelligent learning model. For the problem of multi-semantic audio classification, the experimental results show that our method can significantly improve the classification precision in comparison with the typical method of Gaussian Mixture Model (GMM), and the classification precision of RBFTNN model is obviously better than that of Support Vector Machine (SVM).