Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-20 Cooperative journals: 《计算机应用研究》
Abstract： Texture similarity calculation is one of the basic methods of big-data analysis and data mining. For solving the problem that the existing texture features are not strong for color image discrimination, a texture similarity calculation method with improved local binary pattern features is proposed. This method proposes three modes for feature fusion, including extreme mode, addition mode and encoding mode. The LBP features acquired on the three channels of H, S and V of color image are fused by these modes to obtain the texture description of color image. The fusion operation is carried out in three stages including LBP calculation of neighborhood pixels, LBP calculation of central pixels, and histogram feature extraction, to improve the ability of feature discrimination. The results of texture similarity experiments on VisTex texture database show that, the false acceptance rate, flase rejection rate and equal error rate of this method are obviously lower than those of methods described in references [7, 8, 9].
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-18 Cooperative journals: 《计算机应用研究》
Abstract： In order to meet the needs of the network under the background of big data, and eliminate inferior data interference data knowledge high precision requirements of large data transmission, variable size adjustment scheme based on the algorithm to expand the network of large data knowledge opportunistic scheduling is proposed. Based on the analysis of large data network characteristics, the adaptive vector encoding, capture the heterogeneous characteristics of large data network, using multi order back-propagation network of heterogeneous data is normalized, and then through the real-time transmission of large data network to achieve opportunistic scheduling. At the same time, the knowledge engineering system composed of network data segmentation of fine-grained big data based on the multidimensional feature dimension, the granularity of knowledge transformation is known, then adjust the size of the dynamic characteristics, making big data set of knowledge engineering with linear characteristics and clear geometric characteristics, improve the accuracy of knowledge acquisition through knowledge expansion. The experimental results are compared with the algorithm based on fine grained knowledge acquisition, which proves the high reliability, real time and high efficiency of network data transmission.
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