Object detection through search with a foveated visual system
Fig 9
Performance comparison of the foveated saliency model versus the non-foveated saliency model.
We ran both models for the simple task of identifying the topmost salient location, on 100 natural images randomly selected from the PASCAL VOC 2007 dataset. The blue curve plots the average distance (in degrees) between the topmost salient locations, S1 and S2, found by the foveated and the non-foveated model, respectively, on the same image. Note that this location is unique and fixed for the non-foveated model while it changes for the foveated model as the model explores the image, i.e. makes more and more fixations. The red curve plots the average number of iso-orientation suppression operations of the foveated model relative to that of the non-foveated model. Again, the number of such operations for the non-foveated model is fixed but it changes for the foveated model with the number of fixations. Foveated model finds the same topmost salient location as the non-foveated model, after 16 fixations. Notably, after 8 fixations, the distance between S1 and S2 becomes less than 1 degree. The foveated model achieves this level of accuracy by doing 42% less iso-orientation suppression operations than the non-foveated model.