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

  • Jun Shi
  • Yin Cai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8184)

Abstract

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.

Keywords

Sparse Representation Image Classification Grayscale Image Local Descriptor Sparse Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Jun Shi
    • 1
  • Yin Cai
    • 1
  1. 1.School of Communication and Information EngineeringShanghai UniversityChina

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