  Pattern recognition and classification is a central concern for modern
information processing systems. In particular, one key challenge to image and
video classification has been that the computational cost of image processing
scales linearly with the number of pixels in the image or video. Here we
present an intelligent machine (the "active categorical classifier," or ACC)
that is inspired by the saccadic movements of the eye, and is capable of
classifying images by selectively scanning only a portion of the image. We
harness evolutionary computation to optimize the ACC on the MNIST hand-written
digit classification task, and provide a proof-of-concept that the ACC works on
noisy multi-class data. We further analyze the ACC and demonstrate its ability
to classify images after viewing only a fraction of the pixels, and provide
insight on future research paths to further improve upon the ACC presented
here.
