Convex Coordinates Based on Lattice Independent Sets as Pattern Features

Lattice associative memories have been proposed for image denoising and pattern recognition. We have shown that they can be applied to other domains, like image retrieval and hyperspectral image unsupervised segmentation. In both cases the key idea is that autoassociative morphological memories selective sensitivity to erosive and dilative noise can be applied to detect the lattice independence between patterns. The convex coordinates obtained by linear unmixing based on the sets of lattice independent patterns define a feature extraction process. These features may be useful either for content based image retrieval (CBIR) as well as for pattern classification. We present some CBIR and recognition results on a mushroom shape database, including the comparison with other linear feature extraction algorithms (ICA and CCA).