Description: LBG分类算法
用初始室心随机法和扰动因子分裂法两种方法，比较不同方法不同参数设置时的分类性能。
-LBG classification algorithm vector quantization: vector normalization within a certain range for a particular type, consists of two steps: first generate a codebook, which is the speech feature vector space by the first process- also known as clustering speech parameter sequence as a vector, the reference code for classified- also known as quantization. Clustering algorithm: it is relatively simple and commonly used K-means clustering algorithm. LBG is a clustering algorithm, which is generally assumed that the codebook size is fixed, and for a power of 2. Codebook is small, then expanding until it reaches the requirements. It is often an existing classification split into two subclasses, and initial value with the new code word to each subclass. LBG algorithm on random data and a certain regularity (and meet certain Gaussian distribution) data classification, and look at the performance of the LBG algorithm, the initial chamber heart random disturbance factor-secession law are two Platform: |
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Author:zzc |
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Description: This paper presents a new approach to image segmentation using Pillar K-means algorithm. This
segmentation method includes a new mechanism for grouping the elements of high resolution images in order to
improve accuracy and reduce the computation time. The system uses K-means for image segmentation optimized by
the algorithm after Pillar. The Pillar algorithm considers the placement of pillars should be located as far from each
other to resist the pressure distribution of a roof, as same as the number of centroids between the data distribution. This
algorithm is able to optimize the K-means clustering for image segmentation in the aspects of accuracy and
computation time. This algorithm distributes all initial centroids according to the maximum cumulative distance metric.
This paper evaluates the proposed approach for image segmentation by comparing with K-means clustering
algorithm and Gaussian mixture model and the participation of RGB, HSV, HSL and CIELAB color spaces.
-This paper presents a new approach to image segmentation using Pillar K-means algorithm. This
segmentation method includes a new mechanism for grouping the elements of high resolution images in order to
improve accuracy and reduce the computation time. The system uses K-means for image segmentation optimized by
the algorithm after Pillar. The Pillar algorithm considers the placement of pillars should be located as far from each
other to resist the pressure distribution of a roof, as same as the number of centroids between the data distribution. This
algorithm is able to optimize the K-means clustering for image segmentation in the aspects of accuracy and
computation time. This algorithm distributes all initial centroids according to the maximum cumulative distance metric.
This paper evaluates the proposed approach for image segmentation by comparing with K-means clustering
algorithm and Gaussian mixture model and the participation of RGB, HSV, HSL and CIELAB color spaces.
Platform: |
Size: 974848 |
Author:Deepesh |
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Search - k means clustering for gaussian distribution