Abstract
This paper presents an architecture well suited for natural image classification or visual object recognition applications. The image content is described by a distribution of local prototype features obtained by projecting local signatures on a self-organizing map. The local signatures describe singularities around interest points detected by a wavelet-based salient points detector. Finally, images are classified by using a multilayer perceptron receiving local prototypes distribution as input. This architecture obtains good results both in terms of global classification rates and computing times on different well known datasets.
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Ros, J., Laurent, C., Lefebvre, G. (2006). A Cascade of Unsupervised and Supervised Neural Networks for Natural Image Classification. In: Sundaram, H., Naphade, M., Smith, J.R., Rui, Y. (eds) Image and Video Retrieval. CIVR 2006. Lecture Notes in Computer Science, vol 4071. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11788034_10
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DOI: https://doi.org/10.1007/11788034_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36018-6
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