Abstract: A new feature indexing scheme for binary images is proposed. Using the structures of the conjugate classification of the hexagonal grid, ten intrinsically geometric invariant clusters are identified to partition a binary image into ten feature cluster images. The numbers of feature points in feature images are evaluated. Using the ten integers, a probability model is defined to generate quantitative measurements for feature indexing. This provides intrinsic feature indexing sets for rapid retrieval images based on their contents. Two vectors of twelve probability measurements are used to describe different images in varying sizes and sample pictures and their feature indices are illustrated. !5
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