Abstract
A modification of the well-known PicSOM retrieval system is presented. The algorithm is based on a variant of the self-organizing map algorithm that uses bootstrapping. In bootstrapping the feature space is randomly sampled and a series of subsets are created that are used during the training phase of the SOM algorithm. Afterwards, the resulting SOM networks are merged into one single network which is the final map of the training process. The experimental results have showed that the proposed system yields higher recall-precision rates over the PicSOM architecture.
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Georgakis, A., Li, H. (2006). Content Based Image Retrieval Using a Bootstrapped SOM Network. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_88
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DOI: https://doi.org/10.1007/11760023_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
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