• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural Networks: The Official Journal of the International Neural Network Society >Pixel classification based color image segmentation using quaternion exponent moments
【24h】

Pixel classification based color image segmentation using quaternion exponent moments

机译：使用四元数指数矩的基于像素分类的彩色图像分割

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Image segmentation remains an important, but hard-to-solve, problem since it appears to be application dependent with usually no a priori information available regarding the image structure. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. In this paper, we propose a pixel classification based color image segmentation using quaternion exponent moments. Firstly, the pixel-level image feature is extracted based on quaternion exponent moments (QEMs), which can capture effectively the image pixel content by considering the correlation between different color channels. Then, the pixel-level image feature is used as input of twin support vector machines (TSVM) classifier, and the TSVM model is trained by selecting the training samples with Arimoto entropy thresholding. Finally, the color image is segmented with the trained TSVM model. The proposed scheme has the following advantages: (1) the effective QEMs is introduced to describe color image pixel content, which considers the correlation between different color channels, (2) the excellent TSVM classifier is utilized, which has lower computation time and higher classification accuracy. Experimental results show that our proposed method has very promising segmentation performance compared with the state-of-the-art segmentation approaches recently proposed in the literature. (C) 2015 Elsevier Ltd. All rights reserved.
机译：图像分割仍然是一个重要但难以解决的问题，因为它似乎依赖于应用程序，通常没有关于图像结构的先验信息。近年来，已经开发了许多图像分割算法，但是它们通常非常复杂，并且经常出现一些不良结果。在本文中，我们提出了基于四元数指数矩的基于像素分类的彩色图像分割。首先，基于四元数指数矩（QEM）提取像素级图像特征，通过考虑不同颜色通道之间的相关性，可以有效捕获图像像素内容。然后，将像素级图像特征用作双支持向量机（TSVM）分类器的输入，并通过使用Arimoto熵阈值选择训练样本来训练TSVM模型。最后，用训练后的TSVM模型对彩色图像进行分割。该方案具有以下优点：（1）引入了有效的QEM来描述彩色图像像素内容，其中考虑了不同颜色通道之间的相关性;（2）利用了优良的TSVM分类器，其具有较少的计算时间和较高的分类。准确性。实验结果表明，与文献中最近提出的最新分割方法相比，我们提出的方法具有非常有希望的分割性能。 （C）2015 Elsevier Ltd.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号