Considering the Influence of Visual Saliency during Interface Searches

2012 ◽  
pp. 84-97 ◽  
Author(s):  
Jeremiah D. Still ◽  
Christopher M. Masciocchi
Keyword(s):  
Human Computer Interaction ◽  
Visual Saliency ◽  
Web Pages ◽  
Local Contrast ◽  
Top Down ◽  
Bottom Up ◽  
Rapid Technique ◽  
Computer Interaction

In this chapter, the authors highlight the influence of visual saliency, or local contrast, on users’ searches of interfaces, particularly web pages. Designers have traditionally focused on the importance of goals and expectations (top-down processes) for the navigation of interfaces (Diaper & Stanton, 2004), with little consideration for the influence of saliency (bottom-up processes). The Handbook of Human-Computer Interaction (Sears & Jacko, 2008), for example, does not discuss the influence of bottom-up processing, potentially neglecting an important aspect of interface-based searches. The authors review studies that demonstrate how a user’s attention is rapidly drawn to visually salient locations in a variety of tasks and scenes, including web pages. They then describe an inexpensive, rapid technique that designers can use to identify visually salient locations in web pages, and discuss its advantages over similar methods.

ARTISTIC COMPLEXITY AND SALIENCY: TWO FACES OF THE SAME COIN?

2013 ◽  
Vol 09 (02) ◽  
pp. 1350010 ◽  
Author(s):  
MATTEO CACCIOLA ◽  
GIANLUIGI OCCHIUTO ◽  
FRANCESCO CARLO MORABITO
Keyword(s):  
Information Content ◽  
Visual Saliency ◽  
Relevant Information ◽  
Human Observer ◽  
Top Down ◽  
Complexity Measures ◽  
Bottom Up ◽  
Content Description ◽  
Brain Processing ◽  
The Brain

Many computer vision problems consist of making a suitable content description of images usually aiming to extract the relevant information content. In case of images representing paintings or artworks, the information extracted is rather subject-dependent, thus escaping any universal quantification. However, we proposed a measure of complexity of such kinds of oeuvres which is related to brain processing. The artistic complexity measures the brain inability to categorize complex nonsense forms represented in modern art, in a dynamic process of acquisition that most involves top-down mechanisms. Here, we compare the quantitative results of our analysis on a wide set of paintings of various artists to the cues extracted from a standard bottom-up approach based on visual saliency concept. In every painting inspection, the brain searches for more informative areas at different scales, then connecting them in an attempt to capture the full impact of information content. Artistic complexity is able to quantify information which might have been individually lost in the fruition of a human observer thus identifying the artistic hand. Visual saliency highlights the most salient areas of the paintings standing out from their neighbours and grabbing our attention. Nevertheless, we will show that a comparison on the ways the two algorithms act, may manifest some interesting links, finally indicating an interplay between bottom-up and top-down modalities.

Robust top-down and bottom-up visual saliency for mobile robots using bio-inspired design principles

2021 ◽  
Author(s):  
Uziel Jaramillo-Avila ◽  
Jonathan M. Aitken ◽  
Kevin Gurney ◽  
Sean R. Anderson
Keyword(s):  
Mobile Robots ◽  
Visual Saliency ◽  
Design Principles ◽  
Top Down ◽  
Bottom Up

scholarly journals Inattentional Blindness in Visual Search

2019 ◽  
Author(s):  
Matt Chapman-Rounds ◽  
Christopher G. Lucas ◽  
Frank Keller
Keyword(s):  
Visual Search ◽  
General Model ◽  
Inattentional Blindness ◽  
Visual Saliency ◽  
Top Down ◽  
Bottom Up ◽  
Guided Search ◽  
Search Results ◽  
Task Features

Models of visual saliency normally belong to one of two camps: models such as Experience Guided Search (E-GS), which emphasize top-down guidance based on task features, and models such as Attention as Information Maximisation (AIM), which emphasize the role of bottom-up saliency. In this paper, we show that E-GS and AIM are structurally similar and can be unified to create a general model of visual search which includes a generic prior over potential non-task related objects. We demonstrate that this model displays inattentional blindness, and that blindness can be modulated by adjusting the relative precisions of several terms within the model. At the same time, our model correctly accounts for a series of classical visual search results.

Visual saliency detection via integrating bottom-up and top-down information

Optik ◽  
2019 ◽  
Vol 178 ◽  
pp. 1195-1207 ◽  
Author(s):  
Zahra Sadat Shariatmadar ◽  
Karim Faez
Keyword(s):  
Saliency Detection ◽  
Visual Saliency ◽  
Top Down ◽  
Bottom Up ◽  
Visual Saliency Detection

Psychology and Culture: Top Down Versus Bottom Up?

PsycCRITIQUES ◽  
2005 ◽  
Vol 50 (19) ◽  
Author(s):  
Michael Cole
Keyword(s):  
Top Down ◽  
Bottom Up
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