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Joint Sparse Coding Spatial Pyramid Matching for Classification of Color Blood Cell Image

机译:联合稀疏编码空间金字塔匹配对彩色血细胞图像的分类

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摘要

In the Automatic recognition of blood cell images, the color blood cell images are usually transformed into grayscale images for feature extraction, which result in losing plenty of useful color information. Although the sparse coding based linear spatial pyramid matching (ScSPM) is popular in grayscale image classification, the sparse coding methods in ScSPM fail to extract color information. In this paper, we proposed a novel joint sparse coding SPM (JScSPM) method by using the joint trained joint codebook. The joint codebook is able to represent the inner color correlation among different color components, and the individual color information of each color channel as well. JScSPM method was then applied to classify color blood cell images. The experimental results showed that the proposed method achieved mean 3.1% and 6.6% improvements on classification accuracy, compared with the majority voting based ScSPM the original ScSPM, respectively.
机译:在血细胞图像的自动识别中,彩色血细胞图像通常会转换为用于特征提取的灰度图像,这会导致丢失大量有用的颜色信息。尽管基于稀疏编码的线性空间金字塔匹配(ScSPM)在灰度图像分类中很流行,但是ScSPM中的稀疏编码方法无法提取颜色信息。在本文中,我们提出了一种使用联合训练的联合密码本的新型联合稀疏编码SPM(JScSPM)方法。联合码本能够表示不同颜色分量之间的内部颜色相关性,以及每个颜色通道的各个颜色信息。然后应用JScSPM方法对彩色血细胞图像进行分类。实验结果表明,与基于投票的多数ScSPM和原始ScSPM相比,该方法的分类准确率分别提高了3.1%和6.6%。

著录项

  • 来源
  • 会议地点 Nagoya(JP)
  • 作者

    Jun Shi; Yin Cai;

  • 作者单位

    School of Communication and Information Engineering, Shanghai University;

    School of Communication and Information Engineering, Shanghai University;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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