变权重MRF算法在图像自动无监督分割中的应用

为了实现图像的自动无监督分割,本文提出类自适应变权重马尔可夫随机场分割算法。首先结合最小描述长度准则,自适应计算马尔可夫随机场框架下的图像分类数;然后引入变权重的马尔可夫随机场算法,扩大势函数的选择范围,消除势函数的复杂计算;最后用迭代条件模式进行优化,获得最大后验概率准则下的分割图像。在Matlab环境中的测试结果表明,该算法具有实效性,能正确计算分类数,同时有效减少了分割错误。     

Enhanced neural gas network for prototype-based clustering

In practical cluster analysis tasks, an efficient clustering algorithm should be less sensitive to parameter configurations and tolerate the existence of outliers. Based on the neural gas (NG) network framework, we propose an efficient prototype-based clustering (PBC) algorithm called enhanced neural gas (ENG) network. Several problems associated with the traditional PBC algorithms and original NG algorithm such as sensitivity to initialization, sensitivity to input sequence ordering and the adverse influence from outliers can be effectively tackled in our new scheme. In addition, our new algorithm can establish the topology relationships among the prototypes and all topology-wise badly located prototypes can be relocated to represent more meaningful regions. Experimental results¹on synthetic and UCI datasets show that our algorithm possesses superior performance in comparison to several PBC algorithms and their improved variants, such as hard c-means, fuzzy c-means, NG, fuzzy possibilistic c-means, credibilistic fuzzy c-means, hard/fuzzy robust clustering and alternative hard/fuzzy c-means, in static data clustering tasks with a fixed number of prototypes.     

一种基于MDL度量的选择性扩展贝叶斯分类器

朴素贝叶斯分类器是一种简单而高效的分类器,但它的条件独立性假设使其无法表示属性间的依赖关系。TAN分类器按照一定的结构限制,通过添加扩展弧的方式扩展朴素贝叶斯分类器的结构。在TAN分类器中,类变量是每一个属性变量的父结点,但有些属性的存在降低了它分类的正确率。文中提出一种基于MDL度量的选择性扩展贝叶斯分类器(SANC),通过MDL度量,删除影响分类性能的属性变量和扩展弧。实验结果表明,与NBC和TANC相比,SANC具有较高的分类正确率。     

LEARNING STRUCTURED BAYESIAN NETWORKS: COMBINING ABSTRACTION HIERARCHIES AND TREE-STRUCTURED CONDITIONAL PROBABILITY TABLES

Context-specific independence representations, such as tree-structured conditional probability distributions, capture local independence relationships among the random variables in a Bayesian network (BN). Local independence relationships among the random variables can also be captured by using attribute-value hierarchies to find an appropriate abstraction level for the values used to describe the conditional probability distributions. Capturing this local structure is important because it reduces the number of parameters required to represent the distribution. This can lead to more robust parameter estimation and structure selection, more efficient inference algorithms, and more interpretable models. In this paper, we introduce Tree-Abstraction-Based Search (TABS), an approach for learning a data distribution by inducing the graph structure and parameters of a BN from training data. TABS combines tree structure and attribute-value hierarchies to compactly represent conditional probability tables. To construct the attribute-value hierarchies, we investigate two data-driven techniques: a global clustering method, which uses all of the training data to build the attribute-value hierarchies, and can be performed as a preprocessing step; and a local clustering method, which uses only the local network structure to learn attribute-value hierarchies. We present empirical results for three real-world domains, finding that (1) combining tree structure and attribute-value hierarchies improves the accuracy of generalization, while providing a significant reduction in the number of parameters in the learned networks, and (2) data-derived hierarchies perform as well or better than expert-provided hierarchies.     

长春市大比例尺地形图缩编方法研究与应用

大比例尺地形图作为数字城市基础地理信息系统的主要空间数据,在城市规划管理、交通、公众服务等众多领域有着十分重要的作用。将已有比例尺地形图进行缩编得到需要的比例尺数据,避免了不必要的重复数据采集,节约了资源与成本,也缩短了成图周期,具有重要的意义。本文研究在MicroStation 95环境下运用MDL语言编程实现大比例尺地形图的自动化缩编,并将其用于长春市1∶500、1∶1 000比例尺的全要素地形图缩编为1∶2 000地形图,取得了较好的效果。     

信源数目估计方法的研究

盲信号处理（Blind Signal ProCessing）是近年来信号处理研究的热点之一,＂盲＂是指在没有任何先验信息或者先验信启、不足环境下的假设,因此,该处理方法对环境及信号的应用更加宽泛。盲信源数目是盲分离的首要前提,信源数目的确定更是直接关系到盲分离效果的正确性。本文主要研究了IAC准则,MDL准则和盖式圆准则的盲信源数目估计算法。在不同信噪比情况下,分别对噪声观测信号进行信源数目估计。通过实验证明,盖式圆在含有噪声的情况下,能够相对准确的估计出信源数目,IAC和MDL在低信噪比情况下,不能准确的估计出信源数目。     

基于OMP算法的宽带频谱感知

频谱感知是认知无线电的一项关键技术,其能够检测出未被主用户占用的频谱空穴供次用户接入使用,提高频谱利用率.宽带频谱感知要求对数GHz 的带宽进行检测,过高的采样速率、大的数据量对现有的硬件设备提出了巨大的挑战.本文利用宽带频谱的稀疏性提出一种基于OMP算法的宽带频谱感知方法.该方法利用MWC采样实现对宽带模拟信号直接压缩采样;利用自相关矩阵对称分解特性和主用户信号独立性,得到有限维压缩采样信号模型,利用AIC/MDL准则估计稀疏度作为OMP算法迭代停止的条件,大大减少了算法复杂度;该方法不需要重构接收信号的PSD,直接在时域根据低速率采样信号,检测被占用信道.仿真结果表明,当带内信噪比大于9dB时,频谱检测概率高于90％.