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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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A Cooperative Learning-Based Clustering Approach to Lip Segmentation Without Knowing Segment Number.

Yiu-Ming Cheung, Meng Li, Qinmu Peng

    IEEE Transactions on Neural Networks and Learning Systems
    |December 20, 2015
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    This study introduces a novel clustering approach for lip segmentation that does not require predefining the number of segments. The method effectively identifies the true number of clusters, improving segmentation accuracy compared to existing techniques.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Lip segmentation is challenging due to the difficulty in predetermining the exact number of segments.
    • Existing methods often require prior knowledge of the segment count, limiting their applicability.

    Purpose of the Study:

    • To develop a clustering-based lip segmentation approach that does not require knowing the true number of segments.
    • To introduce a novel objective function based on partition entropy (PE) for robust segmentation.

    Main Methods:

    • A clustering algorithm is proposed using a modified partition entropy objective function.
    • An iterative process adjusts cluster centroids, allowing redundant centroids to merge.
    • The algorithm initializes clusters with a number greater than or equal to the ground truth.

    Main Results:

    • The proposed method successfully determines the optimal number of clusters without prior knowledge.
    • Coincident cluster centroids are effectively handled, simplifying the segmentation process.
    • Empirical studies demonstrate the efficacy of the lip segmentation scheme.

    Conclusions:

    • The developed clustering-based approach offers an effective solution for lip segmentation without needing the true segment number.
    • The method shows improved performance compared to existing lip segmentation techniques.