A received image (1301) is divided into patches, which may have different sizes. A cluster-direction sequence (see fig. 6) is generated (1302) for each of a plurality of saccadic paths along the patches, the paths being given by a policy matrix (see figs. 8 & 9). The sequences are used to identify an object in the image. Path sequence generation using the matrix may comprise assigning (1303) a likelihood (which may be weighted (1304) using a total frequency of occurrence of the sequence in the matrix for a given class) that the image belongs to each class defined in the matrix, and identifying (1305) the object using a likelihood average over the sequences. Image context, e.g. previous observations or goal, may be used. The patches may be clustered into groups, e.g. using k-means clustering algorithm (see figs. 4&5). Specific salient features in an image may thus be identified, leading to a classification of the image by judging whether the salient features can identify a unique class of object (e.g. 9 MNIST digits, or image classes in ImageNet, e.g. cats, planes etc.) in the image with high probability. Classification may occur through progressive exclusion of other classes.
Visual Object and Event Detection and Prediction System using Saccades