起源于并行学习算法对数据划分的要求，在对一种现行等分割聚类算法进行改进的基础上，本文提出自己的等分聚类算法。
Rooted in the requirement of data partitioning in parallel learning, we proposed our cluster method by improving a current clustering equally method.
本文首先分析了基于内容的视频检索的关键技术。总结了镜头分割、视频流特征分析和镜头聚类方面的相关研究和算法。
At first, we summarize the key techniques used in the content-based video retrieval, such as shots division, video character analysis, shots clustering, etc.
本文介绍了实现视频分割和场景聚类的算法。
This paper introduced algorithms for video segmentation and scene clustering.
为了改进当前社会化标注系统在标签浏览和检索方面的弱点，提出一种基于加权网络分割的社会性标签聚类算法。
This paper proposes a clustering algorithm of social tags based on weighed network division for the purpose of improving browsing and retrieval in existing social annotation system.
本文给出了模糊聚类算法在图像分割中的应用结果。
In this paper, the application of suppressed fuzzy clustering algorithm in image segmentation is introduced.
为了对低信噪比的超声图像进行有效分割，提出一种谱聚类集成的超声图像分割算法。
A novel ultrasound image segmentation algorithm, which is based on the spectral cluster ensemble, is proposed to segment ultrasound images with low SNR.
针对偏置环境下图像分割问题，提出了一种基于偏置场估计的模糊聚类算法。
A novel FCM segmentation algorithm is proposed based on bias field estimation with respect to the segmentation issue of defocused images with illumination patterns under bias field.
本文将经典的模糊c -均值聚类算法和模糊测度和模糊积分结合起来，并将这两种算法应用于医学病理图象的分割。
In this article we combine the fuzzy C-means algorithm with fuzzy measures and fuzzy integrals and apply the two algorithms to the medicinal pathological image segmentation.
算法用于图像分割是一种非监督模糊聚类后再标定的过程。
It is a procedure of the label following an unsupervised fuzzy clustering that fuzzy c-means (FCM) algorithm is applied to image segmentation.
结果模糊K- 均值聚类算法能很好地分割出磁共振颅脑图像中的灰质、 白质和脑脊液。
Results Fuzzy K means clustering algorithm can segment white matter, gray matter and CSF better from the MR head images.
目的介绍一种动态模糊聚类算法并利用该算法对磁共振图像进行分割研究。
Objective to introduce a dynamic fuzzy clustering algorithm and use it to do the study of segmentation of the brain in MRI.
针对模糊核聚类对红外图像分割存在的不足，提出了一种改进的模糊核聚类红外图像分割算法。
Due to the problems of infrared image segmentation using fuzzy kernel clustering, an improved method for infrared image segmentation was proposed.
根据视觉的颜色聚类特性，提出一种图像分割算法。
Proposes an image segmentation algorithm based on perceptual color clustering.
算法首先对图像进行量化处理，而后在量化后的色彩空间中集成先验的分割信息进行色彩聚类。
The algorithm first has the image quantized and then clusters in the quantized color space with prior segmentation information.
算法基于标签节点的核心度和相似性对标签共现网络进行分割，并在聚类后自动生成该类的特征标签来代表该类簇。
The algorithm divides tag co -occurrence network based on tag node's centrality and similarity, and automatically generates a cluster feature tag after clustering to represent that cluster.
为了提高单层组织自动识别的精度，运用颜色聚类等方法分割织物样图，并提出了一种经纱分割算法，实现了经纬纱线的准确分割。
Color clustering is used to segment the fabric image, and a new algorithm of warp yarn segmentation is proposed to perform the identification more precisely.
该文根据FCM算法和灰度图像的特点，提出了一种适用于灰度图像分割的抑制式模糊C -均值聚类算法(S - FCM)。
In the paper, a suppressed fuzzy c-means (S-FCM) algorithm, for intensity image segmentation, is proposed on the basis of the characters of FCM algorithm and intensity images.
模糊c -均值聚类是模式识别中的重要算法之一，很早就被应用到图像分割中。
Fuzzy C-means clustering is one of the important learning algorithms in the field of pattern recognition, which has been applied early to image segmentation.
在基于镜头边界的聚类算法基础上，提出了利用镜头帧图像的全局颜色特征和运动特征来分割场景的方法。
Based on the border in the shot clustering algorithm, we proposed the method of using the camera frame color image characteristics and the overall situation of movement to the scene segmentation.
首先，运用K-均值聚类方法提取出细胞核，并且采用多域值分割演算法去除细胞图像中的背景区域。
Firstly, nucleus regions of leukocytes in images are automatically segmented by K-mean clustering method. Then single leukocyte region is detected by utilizing thresholding algorithm segmentation.
前言：目的探讨颅脑mri图像模糊聚类分割算法中最佳模糊聚类数。
Objective: To discuss the best fuzzy clustering number of MRI brain images segmentation.
为了克服传统FCM算法的局限性，本文提出了一种基于空间邻域信息的二维模糊聚类图像分割方法（2DFCM）。
In order to overcome the limitation of FCM, a novel Two-dimension Fuzzy Cluster Method (2DFCM) was proposed based on the spatial information.
经典的C -均值聚类算法（CMA）是将图像分割成C类的常用方法，但依赖于初始聚类中心的选择。
The classical C-means clustering algorithm (CMA) is a well-known clustering method to partition an image into homogeneous regions.
在合理选择冷却进度表的基础上，依据FCM聚类算法建立目标函数，实现了基于SA和FCM聚类的图像分割算法。
Based on choosing reasonable cooling schedule, the objection function for SA is set up according to FCM clustering, and the image segmentation algorithm based on SA and FCM clustering is implemented.
传统的FCM聚类算法未利用图像的空间信息，在分割叠加了噪声的MR图像时分割效果不理想。
However, the segmented results using the conventional FCM when dealing with noisy MR images are not satisfying because FCM takes no spatial information of images into account.
对于区域分割，使用基于加权平方欧式距离的均值聚类算法代替传统的均值聚类算法。
It applies weighted K-means clustering for region segmentation, instead of traditional K-means clustering.
最后用K均值算法对谱聚类集成的结果进行再次聚类，得到最终的集成聚类分割结果。
At last, the segmentation result is clustered again using K-means cluster to get the ultimate segmentation result.
最后用K均值算法对谱聚类集成的结果进行再次聚类，得到最终的集成聚类分割结果。
At last, the segmentation result is clustered again using K-means cluster to get the ultimate segmentation result.
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