Abstract
This paper introduces a new approach to image representation for multimedia databases based on the Self-Organizing Map (SOM) neural network. The distance between each image from a database and the SOM weight vectors trained on the same database is used as a representation for the image. In order to assess the performance of this proposal we compare it with a reference technique in image representation: the Thumbnails method. The results are satisfactory for an initial experiment since it was possible to identify the effectiveness of the SOM-based proposed representation. In order to verify the efficiency of the representations, a classification experiment is performed using the k-NN algorithm. For all image representation experiments, the SOM approach outperforms the Thumbnails reference technique. For example, in one experiment the representation results in a reduction of image size to 2% of its original size and the correct classification rates achieved are 83.33% and 35.42% for SOM and Thumbnails respectively.
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© 2013 Springer-Verlag Berlin Heidelberg
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Silva, L.A., Pazzinato, B., Coelho, O.B. (2013). Image Representation Using the Self-Organizing Map. In: Estévez, P., Príncipe, J., Zegers, P. (eds) Advances in Self-Organizing Maps. Advances in Intelligent Systems and Computing, vol 198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35230-0_14
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DOI: https://doi.org/10.1007/978-3-642-35230-0_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35229-4
Online ISBN: 978-3-642-35230-0
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