Receiving a first image 300 and an indication of one or more sub-regions 302-306 in the first image corresponding to one or more objects of interest; creating an intermediate image (400, Fig 4) comprising one or more random points of a first colour in regions corresponding to the sub-regions, the remainder of the intermediate image having a second colour; and applying a blur (502-506, Fig 5) to the intermediate image to generate an artificial saliency map 600. The artificial saliency map is preferably used with ground truth object classification data related to the first image to train a convolutional neural network to automatically generate new saliency maps from input image data. The saliency maps can then be used for targeted object classification processes on the salient regions of the input images. Preferably the blur is a Gaussian blur and comprising generating an elliptical shaped blur centered on each random point. The artificial saliency map or label image may be a low resolution version 600 of the blurred intermediate image. The intermediate image comprising random points may be a binary image and the number of random points in a sub-region may depend on the size of the sub-region.
Fixation generation for machine learning