Abstract
A multi-net neural computing system is described that can be used for classifying images based on intrinsic image features and extrinsic collateral linguistic description of the contents. A novel representation scheme based on wavelet analysis of images and a subsequent Zernike moment computation helps in a systematic extraction of image features; collateral linguistic description are obtained by the automatic extraction of single and compound keywords. We give a formal description of the system using the Z formal specification notation. An image data set comprising 480 fluorescent stained images of lymphocytes was used in the test of a 3-component unsupervised multi-net neural computing system. The classification accuracy of this system was found to be just over 85%.
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O’Connor, M.F., Hughes, A., Zheng, C., Davies, A., Kelleher, D., Ahmad, K. (2010). Annotation and Retrieval of Cell Images. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osorio, C., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2010. IDEAL 2010. Lecture Notes in Computer Science, vol 6283. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15381-5_27
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DOI: https://doi.org/10.1007/978-3-642-15381-5_27
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