IMAGE RETRIEVAL AND CLASSIFICATION USING ASSOCIATIVE RECIPROCAL-IMAGE ATTRACTORS

In this paper, image processing and symbol processing are bridged with a common framework. A new computational architecture allows arbitrary fixed images to be used as attractors in a general-purpose association processor that can be used for the retrieval and recognition of images. Direct image-to-image associations eliminate the need to extract edges or other features. The creation of attractor basins around the reciprocal-image pairs permits the construction of stable implementations. The algorithms, developed as a neurophysiological model, can form global image associations using only local, recurrent connections. A powerful composite structure can be created with an array of interconnected image processors. We show the results of using this framework successfully and the convergence of partial images to nearby reciprocal-image attractors.