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首页> 外文会议>2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare >Automatic segmentation of fruits in CIELuv color space image using hill climbing optimization and fuzzy C-Means clustering
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Automatic segmentation of fruits in CIELuv color space image using hill climbing optimization and fuzzy C-Means clustering

机译：基于爬山优化和模糊C均值聚类的CIELuv颜色空间图像中的水果自动分割

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摘要

In this paper, a novel method for the segmentation and extraction of natural fruits using Hill climbing (HC) optimization and Modified Fuzzy C-Means (MFCM) clustering algorithm is proposed. The intensity and color information is highly correlated in RGB color images. The segmentation in RGB color space does not produce the meaningful outcome for the segmentation and information retrieval. Many authors have proposed different color space for the segmentation and retrieval of information. In this color based segmentation technique, RGB color images had transformed into perceptually uniform, device independent CIELuv color space for the efficient segmentation. Then for the CIELuv image, the color histogram had generated and computed. This color histogram acts as a search space and has a number of bins. In this work, Hill climbing (HC) optimization had applied for the identification of best image pixels (peaks) which correspond to the initial number of seeds or clusters for the segmentation process. These initial seeds had applied to MFCM for the segmentation of fruits in CIELuv color images. The experimental result had compared with the segmentation process in RGB color space to demonstrate the efficiency of the proposed approach.
机译：提出了一种利用爬山（HC）优化和改进的模糊C-均值（MFCM）聚类算法对天然水果进行分割和提取的新方法。强度和颜色信息在RGB彩色图像中高度相关。 RGB颜色空间中的分割不会为分割和信息检索产生有意义的结果。许多作者针对信息的分割和检索提出了不同的色彩空间。在这种基于颜色的分割技术中，RGB彩色图像已转换为感知均匀，独立于设备的CIELuv色彩空间，以实现有效的分割。然后对于CIELuv图像，已生成并计算了颜色直方图。该颜色直方图充当搜索空间，并具有多个bin。在这项工作中，爬山（HC）优化已应用于识别最佳图像像素（峰值），该像素对应于分割过程中种子或簇的初始数量。这些初始种子已应用于MFCM，用于CIELuv彩色图像中的水果分割。实验结果与RGB色彩空间中的分割过程进行了比较，证明了该方法的有效性。

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