An intelligent model for visual scene analysis and compression

Abstract EN

This paper presents an improved approach for indicating visually salient regions of an image based upon a known visual search task.

The proposed approach employs a robust model of instantaneous visual attention (i.

e.

“bottom-up”) combined with a pixel probability map derived from the automatic detection of a previously-seen object (task-dependent i.

e.

top-down”).

The objects to be recognized are parameterized quickly in advance by a viewpoint-invariant spatial distribution of Speeded up Robust Features (SURF) interest-points.

The bottom-up and top-down object probability images are fused to produce a task-dependent saliency map.

The proposed approach is validated using observer eye-tracker data collected under object search-and-count tasking.

Proposed approach shows 13 % higher overlap with true attention areas under task compared to bottom-up saliency alone.

The new combined saliency map is further used to develop a new intelligent compression technique which is an extension of Discrete Cosine Transform (DCT) encoding.

The proposed approach is demonstrated on surveillance-style footage throughout.