提出了一种基于低密度分割几何距离的半监督KFDA(kernelFisherdiscriminantanalysis)算法(semisupervised KFDA based on low density separation geometry distance,简称Semi GKFDA).该算法以低密度分割几何距离作为相似性度量,通过大...提出了一种基于低密度分割几何距离的半监督KFDA(kernelFisherdiscriminantanalysis)算法(semisupervised KFDA based on low density separation geometry distance,简称Semi GKFDA).该算法以低密度分割几何距离作为相似性度量,通过大量无标签样本,提高KFDA算法的泛化能力.首先,利用核函数将原始空间样本数据映射到高维特征空间中;然后,通过有标签样本和无标签样本构建低密度分割几何距离测度上的内蕴结构一致性假设,使其作为正则化项整合到费舍尔判别分析的目标函数中;最后,通过求解最小化目标函数获得最优投影矩阵.人工数据集和UCI数据集上的实验表明,该算法与KFDA及其改进算法相比,在分类性能上有显著提高.此外,将该算法与其他算法应用到人脸识别问题中进行对比,实验结果表明,该算法具有更高的识别精度.展开更多
Aiming at the rapid identification of rural buildings in complex environments from high-spatialresolution images, an improved Mahalanobis distance colour segmentation method(IMDCSM) is proposed and realised in Red, Gr...Aiming at the rapid identification of rural buildings in complex environments from high-spatialresolution images, an improved Mahalanobis distance colour segmentation method(IMDCSM) is proposed and realised in Red, Green and Blue(RGB) space. Vector sets of a lower discrete degree are obtained by filtering the colour vector sets of the building samples, and a standard ellipsoid equation can be constructed based on these vector sets. The threshold of interested colour range can be flexibly and intuitively selected by changing the shape and size of this ellipsoid. Then, according to the relationship between the location of the image pixel colour vector and the ellipsoid, all building information can be extracted quickly. To verify the effectiveness of the proposed method, unmanned aerial vehicle(UAV) images of two areas in the suburbs of Chengdu city and Deyang city were utilised as experimental data for image segmentation, and the existing colour segmentation method based on the Mahalanobis distance was selected as an indicator to assess the effectiveness of this method. The experimental results demonstrate that the completeness and correctness of this method reached 95% and 83.0%, respectively, values that are higher than those of the Mahalanobis distance colour segmentation method(MDCSM). In general, this method is suitable for the rapid extraction of rural building information, and provides a new threshold selection method for classification.展开更多
文摘提出了一种基于低密度分割几何距离的半监督KFDA(kernelFisherdiscriminantanalysis)算法(semisupervised KFDA based on low density separation geometry distance,简称Semi GKFDA).该算法以低密度分割几何距离作为相似性度量,通过大量无标签样本,提高KFDA算法的泛化能力.首先,利用核函数将原始空间样本数据映射到高维特征空间中;然后,通过有标签样本和无标签样本构建低密度分割几何距离测度上的内蕴结构一致性假设,使其作为正则化项整合到费舍尔判别分析的目标函数中;最后,通过求解最小化目标函数获得最优投影矩阵.人工数据集和UCI数据集上的实验表明,该算法与KFDA及其改进算法相比,在分类性能上有显著提高.此外,将该算法与其他算法应用到人脸识别问题中进行对比,实验结果表明,该算法具有更高的识别精度.
基金Supported by the National Natural Science Foundation of China under Grant No.60473140(国家自然科学基金)the National HighTech Research and Development Plan of China under Grant No.2006AA01Z154(国家高技术研究发展计划(863))+1 种基金the Program for New Century Excellent Talents in University under Grant No.NCET050287(新世纪优秀人才支持计划)the National 985 Project of China under Grant No.9852DBC03(国家985工程)
基金supported by National Science and Technology Support Project of the 12th Five-Year Plan of China (Grant No.2014BAL01B04)Sichuan Provincial Department of Land and Resources Research Project (Grant No.KJ-2018-13)
文摘Aiming at the rapid identification of rural buildings in complex environments from high-spatialresolution images, an improved Mahalanobis distance colour segmentation method(IMDCSM) is proposed and realised in Red, Green and Blue(RGB) space. Vector sets of a lower discrete degree are obtained by filtering the colour vector sets of the building samples, and a standard ellipsoid equation can be constructed based on these vector sets. The threshold of interested colour range can be flexibly and intuitively selected by changing the shape and size of this ellipsoid. Then, according to the relationship between the location of the image pixel colour vector and the ellipsoid, all building information can be extracted quickly. To verify the effectiveness of the proposed method, unmanned aerial vehicle(UAV) images of two areas in the suburbs of Chengdu city and Deyang city were utilised as experimental data for image segmentation, and the existing colour segmentation method based on the Mahalanobis distance was selected as an indicator to assess the effectiveness of this method. The experimental results demonstrate that the completeness and correctness of this method reached 95% and 83.0%, respectively, values that are higher than those of the Mahalanobis distance colour segmentation method(MDCSM). In general, this method is suitable for the rapid extraction of rural building information, and provides a new threshold selection method for classification.
