NATAS: Neural Activity Trace Aware Saliency

Saliency detection has raised much interest in computer vision recently. Many visual saliency models have been developed for individual images, video clips, and image pairs. However, image sequence, one most general occasion in the real world, is not explored yet. A general image sequence is different from video clips whose temporal continuity is maintained and image pairs where common objects exist. It might contain some similar low-level properties while completely distinct contents. Traditional saliency detection methods will fail on these general sequences. Based on this consideration, this paper investigates the shortcomings of the classical saliency detection methods, which significantly limit their advantages: 1) inability to capture the natural connections among sequential images, 2) over-reliance on motion cues, and 3) restriction to image pairs/videos with common objects. In order to address these problems, we propose a framework that performs the following contributions: 1) construct an image data set as benchmark through a rigorously designed behavioral experiment, 2) propose a neural activity trace aware saliency model to capture the general connections among images, and 3) design a novel measure to handle the low-level clues contained among sequential images. Experimental results demonstrate that the proposed saliency model is associated with a tremendous advancement compared with traditional methods when dealing with the general image sequence.