The use of visual information in natural scenes

McCotter, Maxine V; Gosselin, Frédéric; Sowden, Paul T.; Schyns, Philippe G.

doi:10.1080/13506280444000599

Cited by 54 publications

(38 citation statements)

References 43 publications

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“…It is important to note that the cut-off frequency used to filter LSF scenes in the present study (4 cpd, i.e., 24 cpi) was relatively high as compared to the ones used in previous studies (usually around 2 cpd; see Kauffmann, Bourgin, et al, 2015;Mu & Li, 2013;Schyns & Oliva, 1994). LSF scenes in the present study thus included a rather large part of the scene spatial frequency spectrum including low to intermediate spatial frequencies and therefore contained the most diagnostic features for scene and object categorization, which have be found to lie at 0-4 cpd (see Caplette et al, 2014;McCotter et al, 2005). Therefore, it is likely that the advantage for LSF processing observed in the present study also reflects the fact that the LSF scenes contained the most relevant information for categorization.…”

Section: Low Spatial Frequency Processing Advantagementioning

confidence: 47%

“…For example, different spatial frequency bands would be used according to their diagnosticity to categorize a specific visual stimulus. It has been shown that spatial frequencies below 2 cpd are diagnostic to perform basic-level categorization of scenes (e.g., forest, highway, mountain; McCotter, Gosselin, Sowden, & Schyns, 2005). However, intermediate spatial frequencies of 2.3-4 cpd would be required for basic-level categorization of objects (Caplette et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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How does information from low and high spatial frequencies interact during scene categorization?

et al. 2017

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Current models of visual perception suggest that, during scene categorization, low spatial frequencies (LSF) are rapidly processed and activate plausible interpretations of visual input. This coarse analysis would be used to guide subsequent processing of high spatial frequencies (HSF). The present study aimed to further examine how information from LSF and HSF interact and influence each other during scene categorization. In a first experimental session, participants had to categorize LSF and HSF filtered scenes belonging to two different semantic categories (artificial vs. natural). In a second experimental session, we used hybrid scenes as stimuli made by combining LSF and HSF from two different scenes which were semantically similar or dissimilar. Half of the participants categorized LSF scenes in hybrids, and the other half categorized HSF scenes in hybrids. Stimuli were presented for 30 or 100 ms. Session 1 results showed better performance for LSF than HSF scene categorization. Session 2 scene categorization was faster when participants attended and categorized LSF than HSF scene in hybrids. The semantic interference of a semantically dissimilar HSF scene on LSF scene categorization was greater than the semantic interference of a semantically dissimilar LSF scene on HSF scene categorization, irrespective of exposure duration. These results suggest a LSF advantage for scene categorization, and highlight the prominent role of HSF information when there is uncertainty about the visual stimulus, in order to disentangle between alternative interpretations. ARTICLE HISTORY

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Section: Low Spatial Frequency Processing Advantagementioning

confidence: 47%

Section: Introductionmentioning

confidence: 99%

How does information from low and high spatial frequencies interact during scene categorization?

et al. 2017

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“…Visual search is facilitated when there is a correlation across different trials between the contextual configuration of the scene display and the target location (Brockmole & Henderson, 2006b;Chun & Jiang, 1998Eckstein et al, in press;Hidalgo-Sotelo et al, 2005;Jiang & Wagner, 2004;Oliva et al, 2004;Olson & Chun, 2002). In a similar vein, several studies support the idea that scene semantics can be available early in the chain of information processing (Potter, 1976) and suggest that scene recognition may not require object recognition as a first step (Fei-Fei & Perona, 2005;Greene & Oliva, 2006;McCotter et al, 2005;Oliva & Torralba, 2001;Schyns & Oliva, 1994). The present approach proposes a feedforward processing of context (see Figure 1) that is independent of object-related processing mechanisms.…”

Section: Discussionmentioning

confidence: 98%

Contextual guidance of eye movements and attention in real-world scenes: The role of global features in object search.

Torralba

Oliva

Castelhano³

et al. 2006

Psychological Review

1,462

1,361

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Many experiments have shown that the human visual system makes extensive use of contextual information for facilitating object search in natural scenes. However, the question of how to formally model contextual influences is still open. On the basis of a Bayesian framework, the authors present an original approach of attentional guidance by global scene context. The model comprises 2 parallel pathways; one pathway computes local features (saliency) and the other computes global (scenecentered) features. The contextual guidance model of attention combines bottom-up saliency, scene context, and top-down mechanisms at an early stage of visual processing and predicts the image regions likely to be fixated by human observers performing natural search tasks in real-world scenes.Keywords: eye movements, visual search, context, global feature, Bayesian model According to feature-integration theory (Treisman & Gelade, 1980), the search for objects requires slow serial scanning because attention is necessary to integrate low-level features into single objects. Current computational models of visual attention based on saliency maps have been inspired by this approach, as it allows a simple and direct implementation of bottom-up attentional mechanisms that are not task specific. Computational models of image saliency (Itti, Koch, & Niebur, 1998;Koch & Ullman, 1985;Parkhurst, Law, & Niebur, 2002;Rosenholtz, 1999) provide some predictions about which regions are likely to attract observers' attention. These models work best in situations in which the image itself provides little semantic information and in which no specific task is driving the observer's exploration. In real-world images, the semantic content of the scene, the co-occurrence of objects, and task constraints have been shown to play a key role in modulating where attention and eye movements go

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“…For example, many studies have presented observers with a target class of scenes, such as scenes containing animals (Thorpe et al, 1996) or forest scenes (Greene & Oliva, 2009), and have asked observers to detect target scenes among the nontarget distractor scenes. However, such explicit categorization tasks provide a strong top-down signal biasing visual processing toward features that are diagnostic of the target class (Johnson & Olshausen, 2003;McCotter, Gosselin, Sowden, & Schyns, 2005). In other words, if an observer reports seeing (e.g.)…”

mentioning

confidence: 99%

What you see is what you expect: rapid scene understanding benefits from prior experience

Greene

Botros

Beck

et al. 2015

Atten Percept Psychophys

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Although we are able to rapidly understand novel scene images, little is known about the mechanisms that support this ability. Theories of optimal coding assert that prior visual experience can be used to ease the computational burden of visual processing. A consequence of this idea is that more probable visual inputs should be facilitated relative to more unlikely stimuli. In three experiments, we compared the perceptions of highly improbable real-world scenes (e.g., an underwater press conference) with common images matched for visual and semantic features. Although the two groups of images could not be distinguished by their low-level visual features, we found profound deficits related to the improbable images: Observers wrote poorer descriptions of these images (Exp. 1), had difficulties classifying the images as unusual (Exp. 2), and even had lower sensitivity to detect these images in noise than to detect their more probable counterparts (Exp. 3). Taken together, these results place a limit on our abilities for rapid scene perception and suggest that perception is facilitated by prior visual experience.

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The use of visual information in natural scenes

Cited by 54 publications

References 43 publications

How does information from low and high spatial frequencies interact during scene categorization?

How does information from low and high spatial frequencies interact during scene categorization?

Contextual guidance of eye movements and attention in real-world scenes: The role of global features in object search.

What you see is what you expect: rapid scene understanding benefits from prior experience

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