Search In Scenes Research Articles

Does Context act like a Spatial Attentional Set?: Exploring attentional control during visual search in scenes.

Does Context act like a Spatial Attentional Set?: Exploring attentional control during visual search in scenes.

On-Line Contributions of Peripheral Information to Visual Search in Scenes: Further Explorations of Object Content and Scene Context

On-Line Contributions of Peripheral Information to Visual Search in Scenes: Further Explorations of Object Content and Scene Context

The influence of Experience upon Threat Assessment and Visual Search in Complex Scenes

The influence of Experience upon Threat Assessment and Visual Search in Complex Scenes

Predicting Performance in Natural Scene Searches

Predicting Performance in Natural Scene Searches

Predicting performance in natural scene searches

Predicting performance in natural scene searches

Visual search in natural scenes explained by local color properties

Success in visually searching for a small object or target in a natural scene depends on many factors, including the spatial structure of the scene and the pattern of observers' eye movements. The aim of this study was to determine to what extent local color properties of natural scenes can account for target-detection performance. A computer-controlled high-resolution color monitor was used to present images of natural scenes containing a small, randomly located, shaded gray sphere, which served as the target. Observers' gaze position was simultaneously monitored with an infrared video eye-tracker. About 60% of the adjusted variance in observers' detection performance was accounted for by local color properties, namely, lightness and the red-green and blue-yellow components of chroma. A similar level of variance was accounted for by observers' fixations. These results suggest that local color can be as influential as gaze position in determining observers' search performance in natural scenes.

When does repeated search in scenes involve memory? Looking at versus looking for objects in scenes.

One might assume that familiarity with a scene or previous encounters with objects embedded in a scene would benefit subsequent search for those items. However, in a series of experiments we show that this is not the case: When participants were asked to subsequently search for multiple objects in the same scene, search performance remained essentially unchanged over the course of searches despite increasing scene familiarity. Similarly, looking at target objects during previews, which included letter search, 30 seconds of free viewing, or even 30 seconds of memorizing a scene, also did not benefit search for the same objects later on. However, when the same object was searched for again memory for the previous search was capable of producing very substantial speeding of search despite many different intervening searches. This was especially the case when the previous search engagement had been active rather than supported by a cue. While these search benefits speak to the strength of memory-guided search when the same search target is repeated, the lack of memory guidance during initial object searches-despite previous encounters with the target objects-demonstrates the dominance of guidance by generic scene knowledge in real-world search.

Non-selective and Selective Processing in Human Visual Search

Event Abstract Back to Event Non-selective and Selective Processing in Human Visual Search Karla K. Evans1, 2* 1 Harvard Medical School, United States 2 Brigham and Women's Hospital, United States Human’s ability to understand complex visual scenes and effectively find visual targets involves rapid assessment of “gist” of the image as well as scrutiny of the image details. When humans search for visual targets in a complex scene it is thought that they engage two types of processing (non-selective and selective processing)(Wolfe et al., 2011, Evans et al., 2011) that involve deployment of two types of spatial attention (distributed and focused) (Treisman, 2006, Chong & Evans, 2011). I will describe two very relevant phenomenon that affect and guide human visual search in real world scenes involving the two types of processing. One part of the research investigating non-selective processing will show that initial gist processing is based on extraction by the observers of global structural and statistical regularities automatically and flexibly during the first 20-250 milliseconds of visual search. The second part of research investigating selective processing will demonstrate a critical role of visual target prevalence on real world searches such as medical screening. In hard visual search tasks that are performed every day in the clinic we find greatly elevated miss errors at low prevalence as compared to lower miss errors when then target appears at least 50% of the time (“high prevalence”). These results indicate that the low prevalence of disease is actually an important contributor to missed detection of cancer in medical screening. This prevalence effect is adaptive in most real world settings, where an observer should be reluctant to assert that she has found a very rare target. Acknowledgements This research was funded by Ruth L. Kirschstein National Research Service Award Grant F32EY019819-01 to Karla K. Evans. Keywords: Attention, non-selective processing, Prevalence, scene categorization, selective processing, visual search Conference: Tenth International Congress of Neuroethology, College Park. Maryland USA, United States, 5 Aug - 10 Aug, 2012. Presentation Type: Invited Symposium (only for people who have been invited to a particular symposium) Topic: Sensory: Vision Citation: Evans KK (2012). Non-selective and Selective Processing in Human Visual Search. Conference Abstract: Tenth International Congress of Neuroethology. doi: 10.3389/conf.fnbeh.2012.27.00015 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 03 Apr 2012; Published Online: 07 Jul 2012. * Correspondence: Dr. Karla K Evans, Harvard Medical School, Cambridge, Massachusetts, 02139, United States, karla.evans@york.ac.uk Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Karla K Evans Google Karla K Evans Google Scholar Karla K Evans PubMed Karla K Evans Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

3DSSE – A 3D Scene Search Engine

The evolution of real time 3D graphics technologies in combination with high bandwidth Internet connections and modern Web browsers enable users to explore complex 3D scenes. As a rule, a virtual visitor has to manually explore the geometrically complex 3D model in order to discover points of interest. This manual exploration is a time consuming process that, in some cases, can be assisted by sets of predefined points of interest. In this paper, we propose the annotation of 3D scenes in order to equip the user with a text based 3D scene search engine. The search engine provides a query mechanism that unburdens the user from the time consuming process of manually exploring vast 3D scenes. It responds to queries by exploiting the metadata of each 3D model and returns textual and visual information along with a group of links that correspond to relative points of interest within the 3D scene. The search engine allows the virtual visitor to automatically be transferred to a specific point of interest. We have built a Web accessible prototype system that is able to handle queries related to historical data, topological relationships and architectural properties of buildings. A number of 3D reconstructions covering urban areas of cultural importance located in Northern Greece have been annotated and used in the search engine as case studies. The prototype system is based on open source technologies and on a hybrid metadata schema that is derived from the MIDAS Heritage and MACE schemas.

Does repeated search in scenes need memory? When contextual guidance fails, memory takes over

Does repeated search in scenes need memory? When contextual guidance fails, memory takes over

Depth and Size Information Reduce Effective Set Size for Visual Search in Real-World Scenes

Depth and Size Information Reduce Effective Set Size for Visual Search in Real-World Scenes

Guidance during Visual Search in Real-World Scenes: Scene Context vs. Object Content

Guidance during Visual Search in Real-World Scenes: Scene Context vs. Object Content

Depth and size information reduce effective set size for visual search in real-world scenes

Depth and size information reduce effective set size for visual search in real-world scenes

Does oculomotor inhibition of return influence fixation probability during scene search?

Oculomotor inhibition of return (IOR) is believed to facilitate scene scanning by decreasing the probability that gaze will return to a previously fixated location. This "foraging" hypothesis was tested during scene search and in response to sudden-onset probes at the immediately previous (one-back) fixation location. The latencies of saccades landing within 1º of the previous fixation location were elevated, consistent with oculomotor IOR. However, there was no decrease in the likelihood that the previous location would be fixated relative to distance-matched controls or an a priori baseline. Saccades exhibit an overall forward bias, but this is due to a general bias to move in the same direction and for the same distance as the last saccade (saccadic momentum) rather than to a spatially specific tendency to avoid previously fixated locations. We find no evidence that oculomotor IOR has a significant impact on return probability during scene search.

Visual search for arbitrary objects in real scenes

How efficient is visual search in real scenes? In searches for targets among arrays of randomly placed distractors, efficiency is often indexed by the slope of the reaction time (RT) × Set Size function. However, it may be impossible to define set size for real scenes. As an approximation, we hand-labeled 100 indoor scenes and used the number of labeled regions as a surrogate for set size. In Experiment 1, observers searched for named objects (a chair, bowl, etc.). With set size defined as the number of labeled regions, search was very efficient (~5ms/item). When we controlled for a possible guessing strategy in Experiment 2, slopes increased somewhat (~15ms/item), but they were much shallower than search for a random object among other distinctive objects outside of a scene setting (Exp. 3: ~40ms/item). In Experiments 4-6, observers searched repeatedly through the same scene for different objects. Increased familiarity with scenes had modest effects on RTs, while repetition of target items had large effects (>500ms). We propose that visual search in scenes is efficient because scene-specific forms of attentional guidance can eliminate most regions from the "functional set size" of items that could possibly be the target.

Semantic guidance of eye movements in real-world scenes

The perception of objects in our visual world is influenced by not only their low-level visual features such as shape and color, but also their high-level features such as meaning and semantic relations among them. While it has been shown that low-level features in real-world scenes guide eye movements during scene inspection and search, the influence of semantic similarity among scene objects on eye movements in such situations has not been investigated. Here we study guidance of eye movements by semantic similarity among objects during real-world scene inspection and search. By selecting scenes from the LabelMe object-annotated image database and applying latent semantic analysis (LSA) to the object labels, we generated semantic saliency maps of real-world scenes based on the semantic similarity of scene objects to the currently fixated object or the search target. An ROC analysis of these maps as predictors of subjects’ gaze transitions between objects during scene inspection revealed a preference for transitions to objects that were semantically similar to the currently inspected one. Furthermore, during the course of a scene search, subjects’ eye movements were progressively guided toward objects that were semantically similar to the search target. These findings demonstrate substantial semantic guidance of eye movements in real-world scenes and show its importance for understanding real-world attentional control.

Parallel visual search and rapid animal detection in natural scenes

Human observers are capable of detecting animals within novel natural scenes with remarkable speed and accuracy. Recent studies found human response times to be as fast as 120 ms in a dual-presentation (2-AFC) setup (H. Kirchner & S. J. Thorpe, 2005). In most previous experiments, pairs of randomly chosen images were presented, frequently from very different contexts (e.g., a zebra in Africa vs. the New York Skyline). Here, we tested the effect of background size and contiguity on human performance by using a new, contiguous background image set. Individual images contained a single animal surrounded by a large, animal-free image area. The image could be positioned and cropped in such a manner that the animal could occur in one of eight evenly spaced positions on an imaginary circle (radius 10-deg visual angle). In the first (8-Choice) experiment, all eight positions were used, whereas in the second (2-Choice) and third (2-Image) experiments, the animals were only presented on the two positions to the left and right of the screen center. In the third experiment, additional rectangular frames were used to mimic the conditions of earlier studies. Average latencies on successful trials differed only slightly between conditions, indicating that the number of possible animal locations within the display does not affect decision latency. Detailed analysis of saccade targets revealed a preference toward both the head and the center of gravity of the target animal, affecting hit ratio, latency, and the number of saccades required to reach the target. These results illustrate that rapid animal detection operates scene-wide and is fast and efficient even when the animals are embedded in their natural backgrounds.

Looking back at Waldo: Oculomotor inhibition of return does not prevent return fixations

Inhibition of Return (IOR) is a difficulty in processing stimuli presented at recently attended locations. IOR is widely believed to facilitate foraging of a visual scene by decreasing the probability that gaze will return to previously fixated locations. However, there is a lack of clear evidence in support of the foraging facilitator hypothesis during scene search. The original R. M. Klein and W. J. MacInnes' (1999) Where's Waldo study reported a forward bias in the distribution of fixations that was taken as evidence for the foraging facilitator hypothesis. The present study was designed to replicate R. M. Klein and W. J. MacInnes' (1999) but include detailed analysis of fixation distributions in order to test the precise predictions of the foraging facilitator hypothesis. The results indicate that latencies of saccades returning to 1-back (and possibly 2-back) locations during visual search are elevated. However, there is no evidence that the probability of returning to these locations is significantly less than control locations. Eye movement behavior during search of visual scenes does not support the view that IOR facilitates foraging.

The category level effect in the scene search

The category level effect in the scene search

The Dynamics of Top-Down and Bottom-Up Control of Visual Attention during Search in Complex Scenes

The Dynamics of Top-Down and Bottom-Up Control of Visual Attention during Search in Complex Scenes