A visual neural network that learns perceptual relationships

We demonstrate the ability of our recently devised “visual neural network” to display clusters in a database of multidimensional vectors, and to partition this display into decision regions. The multidimensional vectors may be feature vectors from an initial database of images, signals, or other data. The decision regions may be the classes into which the user assigns these data. The decision regions define classes that may only partially overlap some of the clusters. Thus the visual neural network classifies each vector in the database and at the same time maps the vector into a point in a two-dimensional display. When points in the display correspond to perceptually similar items in the initial data. When presented with a “query” from the initial data, a user call search for items similar to the query by examining the raw data represented by points in the same class as the query or by examining nearby points. In this way a user can efficiently browse a database and be assured of accessing related items. Information gained from browsing the database can be used to evaluate the network´s classification decision. We show in an application to medical radiology that a visual neural network can learn a good compromise between perceptual grouping and classification