共查询到6条相似文献,搜索用时 78 毫秒
1.
在图像挖掘中,最关键的步骤是提取图像特征并对之进行描述和评价;在介绍Zernike矩的基础上,指出可以使用Zernike矩集描述图像的形状特征;根据Zernike矩逆变换,可以得到基于Zernike矩形状特征集的图像重构技术,从而通过重构图像与原图像的相异度和重构率来对Zernike矩特征集描述图像形状特征的精确度进行评价;实验结果证明了基于Zernike矩描述图像形状特征与基于图像重构进行评价的可行性。 相似文献
2.
3.
稀疏编码已经广泛应用于复数图像的降噪问题,其中,近些年提出的分组稀疏编码由于能够充分利用同一分组图像块的相似性,在滤除噪声和提高降噪信噪比方面具有更大的优势.研究了一种基于K-means聚类方法的复数图像分组稀疏降噪算法,通过改进聚类算法,验证了K-means算法对分组稀疏编码算法的分组有效性.采用在线复数词典训练算法快速获取编码字典,并运用分组正交匹配追踪算法,实现了分组图像块的稀疏编码.通过限制每一分组图像块中编码的相似性,有效抑制了对图像块中噪声的编码,提高了对复数图像的降噪效果.为验证算法的有效性,对模拟和真实的干涉合成孔径雷达图像的仿真噪声进行了定量分析,证明了所提算法相对于以前的分组稀疏编码算法在峰值信噪比指标上有一定的提升.最后对真实的干涉合成孔径雷达图像进行了降噪,进一步验证了所提降噪算法对于真实噪声的降噪能力. 相似文献
4.
This paper presents an effective scheme for clustering a huge data set using a PC cluster system, in which each PC is equipped
with a commodity programmable graphics processing unit (GPU). The proposed scheme is devised to achieve three-level hierarchical
parallel processing of massive data clustering. The divide-and-conquer approach to parallel data clustering is employed to
perform the coarse-grain parallel processing by multiple PCs with a message passing mechanism. By taking advantage of the
GPU’s parallel processing capability, moreover, the proposed scheme can exploit two types of the fine-grain data parallelism
at the different levels in the nearest neighbor search, which is the most computationally-intensive part of the data-clustering
process. The performance of our scheme is discussed in comparison with that of the implementation entirely running on CPU.
Experimental results clearly show that the proposed hierarchial parallel processing can remarkably accelerate the data clustering
task. Especially, GPU co-processing is quite effective to improve the computational efficiency of parallel data clustering
on a PC cluster. Although data-transfer from GPU to CPU is generally costly, acceleration by GPU co-processing is significant
to save the total execution time of data-clustering. 相似文献
5.
Clustering is a popular data analysis and data mining technique. A popular technique for clustering is based on k-means such that the data is partitioned into K clusters. However, the k-means algorithm highly depends on the initial state and converges to local optimum solution. This paper presents a new hybrid evolutionary algorithm to solve nonlinear partitional clustering problem. The proposed hybrid evolutionary algorithm is the combination of FAPSO (fuzzy adaptive particle swarm optimization), ACO (ant colony optimization) and k-means algorithms, called FAPSO-ACO–K, which can find better cluster partition. The performance of the proposed algorithm is evaluated through several benchmark data sets. The simulation results show that the performance of the proposed algorithm is better than other algorithms such as PSO, ACO, simulated annealing (SA), combination of PSO and SA (PSO–SA), combination of ACO and SA (ACO–SA), combination of PSO and ACO (PSO–ACO), genetic algorithm (GA), Tabu search (TS), honey bee mating optimization (HBMO) and k-means for partitional clustering problem. 相似文献
6.
Jérôme Gilles 《Journal of Mathematical Imaging and Vision》2007,28(3):285-295
These last few years, image decomposition algorithms have been proposed to split an image into two parts: the structures and the textures. These algorithms are not adapted to the case of noisy images because the textures are corrupted by noise. In this paper, we propose a new model which decomposes an image into three parts (structures, textures and noise) based on a local regularization scheme. We compare our results with the recent work of Aujol and Chambolle. We finish by giving another model which combines the advantages of the two previous ones. 相似文献