Vanishing point attracts gaze in free-viewing and visual search tasks

6 Dec 2015  ·  Ali Borji, Mengyang Feng ·

To investigate whether the vanishing point (VP) plays a significant role in gaze guidance, we ran two experiments. In the first one, we recorded fixations of 10 observers (4 female; mean age 22; SD=0.84) freely viewing 532 images, out of which 319 had VP (shuffled presentation; each image for 4 secs). We found that the average number of fixations at a local region (80x80 pixels) centered at the VP is significantly higher than the average fixations at random locations (t-test; n=319; p=1.8e-35). To address the confounding factor of saliency, we learned a combined model of bottom-up saliency and VP. AUC score of our model (0.85; SD=0.01) is significantly higher than the original saliency model (e.g., 0.8 using AIM model by Bruce & Tsotsos (2009), t-test; p= 3.14e-16) and the VP-only model (0.64, t-test; p= 4.02e-22). In the second experiment, we asked 14 subjects (4 female, mean age 23.07, SD=1.26) to search for a target character (T or L) placed randomly on a 3x3 imaginary grid overlaid on top of an image. Subjects reported their answers by pressing one of two keys. Stimuli consisted of 270 color images (180 with a single VP, 90 without). The target happened with equal probability inside each cell (15 times L, 15 times T). We found that subjects were significantly faster (and more accurate) when target happened inside the cell containing the VP compared to cells without VP (median across 14 subjects 1.34 sec vs. 1.96; Wilcoxon rank-sum test; p = 0.0014). Response time at VP cells were also significantly lower than response time on images without VP (median 2.37; p= 4.77e-05). These findings support the hypothesis that vanishing point, similar to face and text (Cerf et al., 2009) as well as gaze direction (Borji et al., 2014) attracts attention in free-viewing and visual search.

PDF Abstract
No code implementations yet. Submit your code now

Tasks


Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods


No methods listed for this paper. Add relevant methods here