Feature congestion: A measure of visual clutter

Rosenholtz, Ruth; Jin, Zhenlan; Mansfield, Jonathan

doi:10.1167/6.6.827

Cited by 112 publications

(180 citation statements)

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“…Saliency, as a composite measure of low-level visual information, is positively correlated with the presence of objects (Elazary & Itti, 2008), and found to guide attention in tasks where targets are underspecified (e.g., memorization, Underwood & Foulsham, 2006), and also utilized during situated language production (Gleitman et al, 2007). In our view, the most appropriate way to define visual saliency is in terms of visual clutter, calculated by integrating low-level visual information, e.g., color, with edge information (Rosenholtz, Mansfield, & Jin, 2005). The inclusion of edges sets apart clutter from standard visual saliency (Itti & Koch, 2000), which is based solely on low-level features, without taking object-based visual information such as edges into account.…”

Section: Introductionmentioning

confidence: 84%

“…We used Photoshop to paste the target objects (e.g., MAN and CLIPBOARD) into the scene and added distractors to obtain the cluttered version of the same scene. A paired-samples t-test was conducted to test that the visual clutter (measured as Feature Congestion, Rosenholtz et al, 2005) in Minimal scenes (mean = 2.78, SD = 1.12) was significantly smaller than in their Cluttered version (mean = 3.52, SD = 0.33; t(46) = 3.71, p = 0.0005).…”

Section: Methodsmentioning

confidence: 99%

“…To investigate perceptual guidance, we manipulate visual clutter (Rosenholtz et al, 2005), a measure of visual complexity integrating low-level features (e.g., color, intensity, and orientation) and edge information. To test the impact of conceptual guidance, we manipulate the animacy of the cue that orients the speaker towards a particular visual referent to be described.…”

Section: Independent Variables For Investigating Guidancementioning

confidence: 99%

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Integrating mechanisms of visual guidance in naturalistic language production

Coco

Keller

2014

Cogn Process

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Situated language production requires the integration of visual attention and linguistic processing. Previous work has not conclusively disentangled the role of perceptual scene information and structural sentence information in guiding visual attention. In this paper, we present an eye-tracking study that demonstrates that three types of guidance, perceptual, conceptual, and structural, interact to control visual attention. In a cued language production experiment, we manipulate perceptual (scene clutter) and conceptual guidance (cue animacy), and measure structural guidance (syntactic complexity of the utterance). Analysis of the time course of language production, before and during speech, reveals that all three forms of guidance affect the complexity of visual responses, quantified in terms of the entropy of attentional landscapes and the turbulence of scan patterns, especially during speech. We find that perceptual and conceptual guidance mediate the distribution of attention in the scene, whereas structural guidance closely relates to scan-pattern complexity. Furthermore, the eye-voice span of the cued object and its perceptual competitor are similar; its latency mediated by both perceptual and structural guidance. These results rule out a strict interpretation of structural guidance as the single dominant form of visual guidance in situated language production. Rather, the phase of the task and the associated demands of cross-modal cognitive processing determine the mechanisms that guide attention.

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Section: Introductionmentioning

confidence: 84%

Section: Methodsmentioning

confidence: 99%

Section: Independent Variables For Investigating Guidancementioning

confidence: 99%

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Integrating mechanisms of visual guidance in naturalistic language production

Coco

Keller

2014

Cogn Process

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“…According to Wood [36], people perceive a higher complexity with denser and more dissimilar information presentation. With a similar definition in mind, Rosenholtz et al [29] predicted whether maps are perceived as cluttered based on a calculation of several features: colors and luminance, different sizes, shapes, and motions. Clutter, in her definition, increases in tandem to the number of unusual objects human attention is drawn to-a concept that is partially related to visual complexity [30].…”

Section: Symmetrymentioning

confidence: 99%

Predicting users' first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness

Reinecke

Yeh

Miratrix

et al. 2013

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

219

203

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Users make lasting judgments about a website's appeal within a split second of seeing it for the first time. This first impression is influential enough to later affect their opinions of a site's usability and trustworthiness. In this paper, we demonstrate a means to predict the initial impression of aesthetics based on perceptual models of a website's colorfulness and visual complexity. In an online study, we collected ratings of colorfulness, visual complexity, and visual appeal of a set of 450 websites from 548 volunteers. Based on these data, we developed computational models that accurately measure the perceived visual complexity and colorfulness of website screenshots. In combination with demographic variables such as a user's education level and age, these models explain approximately half of the variance in the ratings of aesthetic appeal given after viewing a website for 500ms only.

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“…There are other differences in human vision which may be worth modeling, e.g. light receptor density and distribution, perception of motion, optical flow, visual crowding [17].…”

Section: Resultsmentioning

confidence: 99%

Creating Personalized Digital Human Models of Perception for Visual Analytics

Bennett¹,

Quigley²

2011

User Modeling, Adaption and Personalization

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Abstract. Our bodies shape our experience of the world, and our bodies influence what we design. How important are the physical differences between people? Can we model the physiological differences and use the models to adapt and personalize designs, user interfaces and artifacts? Within many disciplines Digital Human Models and Standard Observer Models are widely used and have proven to be very useful for modeling users and simulating humans. In this paper, we create personalized digital human models of perception (Individual Observer Models), particularly focused on how humans see. Individual Observer Models capture how our bodies shape our perceptions. Individual Observer Models are useful for adapting and personalizing user interfaces and artifacts to suit individual users' bodies and perceptions. We introduce and demonstrate an Individual Observer Model of human eyesight, which we use to simulate 3600 biologically valid human eyes. An evaluation of the simulated eyes finds that they see eye charts the same as humans. Also demonstrated is the Individual Observer Model successfully making predictions about how easy or hard it is to see visual information and visual designs. The ability to predict and adapt visual information to maximize how effective it is is an important problem in visual design and analytics.

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Feature congestion: A measure of visual clutter

Cited by 112 publications

References 0 publications

Integrating mechanisms of visual guidance in naturalistic language production

Integrating mechanisms of visual guidance in naturalistic language production

Predicting users' first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness

Creating Personalized Digital Human Models of Perception for Visual Analytics

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