Color Image Segmentation based on Lattice Auto-Associative Memories

G. Urcid and J.C. Valdiviezo-N. (Mexico)


Color image segmentation, convex sets, lattice associative mem ories, linear mixing model, minimax algebra, RGB color space.


This manuscript introduces a new technique for autonomous segmentation of color images in Red-Green-Blue (RGB) space that makes use of lattice auto-associative memories (LAAMs). LAAMs are artificial neural networks able to store a finite set X of pattern vectors and retrieve them almost correctly when a noisy or incomplete input is presented. Two dual lattice auto-associative memories have been established, the min memory W XX and the max memory MXX whose column vectors, scaled appropriately, are used to determine a tetrahedron enclosing a subset of X. Specifically, the column vectors of each memory will correspond to the most saturated color pixels. Thus, from the perspective of convex set geometry, the scaled column vectors of a LAAM are extreme points of a convex subset of X (tetrahedron), and any other pixel can be considered a linear mixture of these points. Unsupervised segmentation of a color image is then realized by unmixing pixels using the non-negative least square method. We provide illustrative examples to demonstrate the effectiveness of our method as well as segmentation results for some RGB color images.

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