A stochastic neural model for fast classification of binary images
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
| Hi-index | 0.00 |
In a previous work [1], we presented an approach for shape-based image retrieval using the curvature scale space (CSS) and self-organizing map (SOM) methods. Here, we examine the robustness of the representation under affine transforms. Moreover, the CSS images extracted from a database are processed and described by median vectors that constitutes the training data set for a SOM neural network. This way of description improves the accuracy of image retrieval in comparison with the previous work [1] that used the first principal component of the PCA technique. Experiments with a benchmark database are carried out to demonstrate the usefulness of the proposed methodology.