Saliency Maps

My current readingy deals with the creation of saliency maps, topological maps that combine multiscale image features that correspond to attentive selection.

A brief introduction to mapping is provided by Dr. Ernst Niebur here.

For the bottom-up approach, Cristof Koch and Shimon Ullman proposed that different visual features contribute to the stimulus, such as color, intensity, orientation, and movement. The saliency map integrates this information into one global measure, essentially a topographical map. Then the most 'salient' features of the image correspond to the global maximum of this topographical map. (From my reading, the most popular method to accomplish this is a variation of the Gradient Ascent Optimization method, which follows the positive gradient to a local maximum). In the bottom approach, these regions of greatest salience are then considered in sequential order, whereas top-down would override the most salient regions in favor of more relevant areas.

Examples of a saliency map:

The above process: First image is the original picture, the three images correspond to Color, Intensity, and Orientation respectively, and then a final saliency map. Note in practice, a saliency map is often composed of even more feature maps. The final several pictures provide the regions where the focus of attention (FOA) is directed, along with the amount of simulated time to 'notice' these regions (essentially how long it would take for the imaes to pop out to the visual cortex).

A brief paper that outlines a method of creating and using Saliency Maps: A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. Itti, Koch, and Niebur