Abstract
An unsupervised automatic moment-based image recognition technique is provided in this paper. The problem to be solved consists of classifying the images from a set, using the content similarity. In the feature extraction stage, we compute a set of feature vectors using discrete area moments. An automatic unsupervised feature vector classification approach is proposed next. It uses a hierarchical agglomerative clustering algorithm, the optimal number of clusters being determined using some validation indexes. Some experiments performed with the proposed approach are also described in this article.
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Barbu, T., Costin, M., Ciobanu, A. (2013). An Unsupervised Content-Based Image Recognition Technique. In: Balas, V., Fodor, J., Várkonyi-Kóczy, A. (eds) New Concepts and Applications in Soft Computing. Studies in Computational Intelligence, vol 417. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28959-0_9
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DOI: https://doi.org/10.1007/978-3-642-28959-0_9
Publisher Name: Springer, Berlin, Heidelberg
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