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.
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Shi, J., Cai, Y. (2013). Joint Sparse Coding Spatial Pyramid Matching for Classification of Color Blood Cell Image. In: Wu, G., Zhang, D., Shen, D., Yan, P., Suzuki, K., Wang, F. (eds) Machine Learning in Medical Imaging. MLMI 2013. Lecture Notes in Computer Science, vol 8184. Springer, Cham. https://doi.org/10.1007/978-3-319-02267-3_30
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DOI: https://doi.org/10.1007/978-3-319-02267-3_30
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