A Cascade of Unsupervised and Supervised Neural Networks for Natural Image Classification
- Cite this paper as:
- 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
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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