Efficient multilevel image segmentation through fuzzy entropy maximization and graph cut optimization

The fuzzy c-partition entropy has been widely adopted as a global optimization technique for finding the optimized thresholds for multilevel image segmentation. However, it involves expensive computation as the number of thresholds increases and often yields noisy segmentation results since spatial coherence is not enforced. In this paper, an iterative calculation scheme is presented for reducing redundant computations in entropy evaluation. The efficiency of threshold selection is further improved through utilizing the artificial bee colony algorithm as the optimization technique. Finally, instead of performing thresholding for each pixel independently, the presented algorithm oversegments the input image into small regions and uses the probabilities of fuzzy events to define the costs of different label assignments for each region. The final segmentation results is computed using graph cut, which produces smooth segmentation results. The experimental results demonstrate the presented iterative calculation scheme can greatly reduce the running time and keep it stable as the number of required thresholds increases. Quantitative evaluations over 20 classic images also show that the presented algorithm outperforms existing multilevel segmentation approaches.     

Explicit solution of relative entropy weighted control

We consider the minimization over probability measures of the expected value of a random variable, regularized by relative entropy with respect to a given probability distribution. In the general setting we provide a complete characterization of the situations in which a finite optimal value exists and the situations in which a minimizing probability distribution exists. Specializing to the case where the underlying probability distribution is Wiener measure, we characterize finite relative entropy changes of measure in terms of square integrability of the corresponding change of drift. For the optimal change of measure for the relative entropy weighted optimization, an expression involving the Malliavin derivative of the cost random variable is derived. The theory is illustrated by its application to several examples, including the case where the cost variable is the maximum of a standard Brownian motion over a finite time horizon. For this example we obtain an exact optimal drift, as well as an approximation of the optimal drift through a Monte-Carlo algorithm.     

Entropy of fluidized bed—a measure of particles mixing

The paper presents an attempt to define the physical entropy of the dense phase of a fluidized bed, based on liquid-like properties of fluidized systems. The quantity U was used as the analogy of temperature in classical thermodynamics. The obtained expression for physical entropy was compared to the correlation suggested for the Kolmogorov entropy in bubbling bed.     

基于灰熵关联分析的流水车间多目标调度优化及算法实现

求解流水车间多目标调度优化问题及算法适应度值分配问题, 结合灰色关联度分析方法及信息熵理论提出灰熵关联度适应值分配策略, 利用灰关联系数结合熵值权重计算适应度值, 以灰熵关联度值引导启发式算法进化. 将该方法应用到差分算法及遗传算法中解决三目标流水车间调度问题. 实验表明: 灰熵关联度适应值分配策略能够解决该问题, 可以得到分布均匀的Pareto 前端; 同时, 基于此策略的差分算法得到的解好于遗传算法的解.     

一种基于案例的多准则排序方法

针对带有间接偏好信息的多准则决策问题, 首先利用加性效用函数理论提出一种排序方法, 该方法通过构建一个简单的优化模型, 得到与间接偏好信息相容的各评价值的效用; 然后, 利用线性插值方法计算出剩下方案各评价值的效用, 进而得到所有方案的综合效用及排序; 最后, 通过实例验证了该方法的有效性和可行性.

     

在线用户兴趣多样性的实证研究

在线用户的群体兴趣对于分析在线社会网络以及个性化推荐至关重要。研究的目的是引入信息熵这一指标来准确度量用户兴趣的多样性。分别在电影网站MovieLens和音乐网站Last．FM数据上进行实证分析,即统计度相同的用户所选产品的信息熵值。MovieLens的结果表明,随着用户度的增加,熵值出现先上升后下降的趋势,即度小的用户和度大的用户的兴趣比较专一,而一般用户的兴趣较为多样;而Last．FM的结果表明,度小用户的兴趣非常多样,但随着用户听过的音乐数量越多,兴趣越明确。通过建立随机模型与实证结果进行比较,可以发现绝大多数用户在真实数据集上的兴趣的多样性比随机情况要大,可见用户的兴趣对用户行为模式的影响显著。     

基于C-V模型和MRF的脑部MRI分割

为准确分割脑部磁共振图像(MRI)的灰质、白质和背景,提出一种基于C-V模型和马尔可夫随机场的全自动分割方法。采用C-V模型与形态学相结合的方法对脑MRI进行预处理,去除多余脑组织,获得待分割图像。引入灰度场局部熵的思想对惩罚因子进行估计,利用马尔可夫随机场模型建模实现脑灰白质的分割,并运用形态学方法获得最终分割结果。对96幅IBSR图像和46幅临床图像进行实验,结果表明,该方法能够实现脑部MRI灰白质的全自动分割,且具有较好的分割精度和较快的处理速度。     

基于动态特性的D-LTSV语音端点检测方法

端点检测是语音信号处理的一个关键环节。为提高语音在低性噪比以及非平稳噪声环境下的端点检测性能,在长时信号变化特征(LTSV)的基础上提出一种新的D-LTSV语音端点检测方法。采用Bartlett-Welch方法估计语音谱,分析语音谱在长时域上的熵,利用倒谱的动态特性分析方法提取连续帧熵值的动态变化特征。实验结果表明,D-LTSV综合考虑了语音的非平稳性和帧间非平稳性的动态变化情况,具有比LTSV更好的分辨能力,特别是在低性噪比和非平稳噪声的环境下,D-LTSV的分辨能力提升了50.77%,能够准确地进行端点检测,具有更强的鲁棒性。     

基于Q学习的DDoS攻防博弈模型研究

新形势下的DDoS攻防博弈过程和以往不同,因此利用现有的方法无法有效地评估量化攻防双方的收益以及动态调整博弈策略以实现收益最大化。针对这一问题,设计了一种基于Q学习的DDoS攻防博弈模型,并在此基础上提出了模型算法。首先,通过网络熵评估量化方法计算攻防双方收益;其次,利用矩阵博弈研究单个DDoS攻击阶段的攻防博弈过程;最后,将Q学习引入博弈过程,提出了模型算法,用以根据学习效果动态调整攻防策略从而实现收益最大化。实验结果表明,采用模型算法的防御方能够获得更高的收益,从而证明了算法的可用性和有效性。