The algorithm divides tag co -occurrence network based on tag node's centrality and similarity, and automatically generates a cluster feature tag after clustering to represent that cluster.
算法基于标签节点的核心度和相似性对标签共现网络进行分割，并在聚类后自动生成该类的特征标签来代表该类簇。
First selects texture features based on the gray level co-occurrence Matrix and then EBP-OP neural network is used as a classifier. The experimental results show that this method is very effective.
首先运用灰度共生矩阵提取图像的纹理特征，然后用EBP - OP算法对提取的纹理特征进行分类，并在此基础上实现一组纹理图像的检索，实验证明这种方法是有效的。
Ground objects can be effectively recognized by gray co-occurrence vector and gray co - dimension feature vector with BP neural network and Bayesian network, recognition rate of 70%.
利用灰度共生纹理特征向量和灰度共生-差分维数联合特征向量结合BP神经网络和朴素贝叶斯网络都能对地物进行有效识别，识别率在70%以上。
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