Abstract
In this chapter, we design a Co-occurrence Differential Texton (CoDT) feature to represent the local image patches of HEp-2 cell image, and a generative model is built to adaptively characterize the CoDT feature space. We further exploit a more discriminant representation for the HEp-2 cell images based on the adaptive partitioned feature space, and then feed the representation into a linear SVM classifier to identify the staining patterns. Two benchmark datasets are used for evaluation on the classification performance of our proposed method.
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Xu, X., Wu, X., Lin, F. (2017). Defining Feature Space for Image Classification. In: Cellular Image Classification. Springer, Cham. https://doi.org/10.1007/978-3-319-47629-2_7
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DOI: https://doi.org/10.1007/978-3-319-47629-2_7
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