A proto-object based saliency model in three-dimensional space

- A proto-object based saliency model that includes depth information is proposed.
- The model predicts fixated locations in three separate 3D eyetracking datasets.
- Depth information provides a small but significant improvement in model performance.
- The model provides support for a theory of proto-object based attention.

Most models of visual saliency operate on two-dimensional images, using elementary image features such as intensity, color, or orientation. The human visual system, however, needs to function in complex three-dimensional environments, where depth information is often available and may be used to guide the bottom-up attentional selection process. In this report we extend a model of proto-object based saliency to include depth information and evaluate its performance on three separate three-dimensional eye tracking datasets. Our results show that the additional depth information provides a small, but statistically significant, improvement in the model's ability to predict perceptual saliency (eye fixations) in natural scenes. The computational mechanisms of our model have direct neural correlates, and our results provide further evidence that proto-objects help to establish perceptual organization of the scene.

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