  While previous researches in eye fixation prediction typically rely on
integrating low-level features (e.g. color, edge) to form a saliency map,
recently it has been found that the structural organization of these features
into a proto-object representation can play a more significant role. In this
work, we present a computational framework based on deep network to demonstrate
that proto-object representations can be learned from low-resolution image
patches from fixation regions. We advocate the use of low-resolution inputs in
this work due to the following reasons: (1) Proto-objects are computed in
parallel over an entire visual field (2) People can perceive or recognize
objects well even it is in low resolution. (3) Fixations from lower resolution
images can predict fixations on higher resolution images. In the proposed
computational model, we extract multi-scale image patches on fixation regions
from eye fixation datasets, resize them to low resolution and feed them into a
hierarchical. With layer-wise unsupervised feature learning, we find that many
proto-objects like features responsive to different shapes of object blobs are
learned out. Visualizations also show that these features are selective to
potential objects in the scene and the responses of these features work well in
predicting eye fixations on the images when combined with learned weights.
