基于Hadoop云计算平台的图像分类与标注

为有效处理并利用互联网海量的图像和视频数据,提出了一种基于Hadoop云平台的图像分类和标注解决方案。针对如何高效地进行训练集提取这一重要问题,搭建了基于云计算的图像抓取平台,利用互联网的图像资源作为原始数据集,为提取训练集图像提供足够的数据;实现了基于概率潜在语义分析模型的训练集图像提取功能,对原始数据集进行基于主题的聚类,帮助用户快速选取训练集图像;加入了SVM分类模型,利用提取出来的训练集对未标注图像进行分类标注,实现了完整的系统。实验结果表明,该方案能够满足海量图像数据分类和标注的功能和性能需求。

基于稀疏特征选择的立体素描图像建模方法

为了合成出更加清晰的人脸素描画像,文中提出一种基于稀疏表示的自适应图像建模方法。通过从训练数据中提取有效的训练集,降低学习的盲目性和难度,获取更加精确的字典结果。在精确字典结果的基础上,通过控制线性组合系数的非零元素个数,实现测试图像的自适应重构与优化,从而解决传统经典合成算法噪声较大的问题。仿真测试结果表明,文中所提出的方法提取了更多的图像块,能够滤除更多的高频分量,其合成图像的噪声更小,具有更加优秀的平滑性。

Adaptive WNN aerodynamic modeling based on subset KPCA feature extraction

In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles.