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Differentiating Between Many Similar Features using Relational Information in Space and Scale

We present an approach for differentiating between large numbers of similar feature points. The approach employs a learning strategy which utilizes mutual information to yield relational information or structure between feature points. It learns an ordered list of jumps in space and scale which is used for differentiation. To test the viability and potential of the approach, two datasets containing faces and objects were used