Recognition of natural scenes from global properties: seeing the forest without representing the trees

A summary-statistic representation in peripheral vision explains visual crowding

Peripheral vision provides a less faithful representation of the visual input than foveal vision. Nonetheless, we can gain a lot of information about the world from our peripheral vision, for example in order to plan eye movements. The phenomenon of crowding shows that the reduction of information available in the periphery is not merely the result of reduced resolution. Crowding refers to visual phenomena in which identification of a target stimulus is significantly impaired by the presence of nearby stimuli, or flankers. What information is available in the periphery? We propose that the visual system locally represents peripheral stimuli by the joint statistics of responses of cells sensitive to different position, phase, orientation, and scale. This "textural" representation by summary statistics predicts the subjective "jumble" of features often associated with crowding. We show that the difficulty of performing an identification task within a single pooling region using this representation of the stimuli is correlated with peripheral identification performance under conditions of crowding. Furthermore, for a simple stimulus with no flankers, this representation can be adequate to specify the stimulus with some position invariance. This provides evidence that a unified neuronal mechanism may underlie peripheral vision, ordinary pattern recognition in central vision, and texture perception. A key component of our methodology involves creating visualizations of the information available in the summary statistics of a stimulus. We call these visualizations "mongrels" and show that they are highly useful in examining how the early visual system represents the visual input. Mongrels enable one to study the "equivalence classes" of our model, i.e., the sets of stimuli that map to the same representation according to the model.

Bayesian reconstruction of natural images from human brain activity

Recent studies have used fMRI signals from early visual areas to reconstruct simple geometric patterns. Here, we demonstrate a new Bayesian decoder that uses fMRI signals from early and anterior visual areas to reconstruct complex natural images. Our decoder combines three elements: a structural encoding model that characterizes responses in early visual areas, a semantic encoding model that characterizes responses in anterior visual areas, and prior information about the structure and semantic content of natural images. By combining all these elements, the decoder produces reconstructions that accurately reflect both the spatial structure and semantic category of the objects contained in the observed natural image. Our results show that prior information has a substantial effect on the quality of natural image reconstructions. We also demonstrate that much of the variance in the responses of anterior visual areas to complex natural images is explained by the semantic category of the image alone.

Modeling Search for People in 900 Scenes: A combined source model of eye guidance

How predictable are human eye movements during search in real world scenes? We recorded 14 observers' eye movements as they performed a search task (person detection) in 912 outdoor scenes. Observers were highly consistent in the regions fixated during search, even when the target was absent from the scene. These eye movements were used to evaluate computational models of search guidance from three sources: saliency, target features, and scene context. Each of these models independently outperformed a cross-image control in predicting human fixations. Models that combined sources of guidance ultimately predicted 94% of human agreement, with the scene context component providing the most explanatory power. None of the models, however, could reach the precision and fidelity of an attentional map defined by human fixations. This work puts forth a benchmark for computational models of search in real world scenes. Further improvements in modeling should capture mechanisms underlying the selectivity of observer's fixations during search.

The briefest of glances: the time course of natural scene understanding

What information is available from a brief glance at a novel scene? Although previous efforts to answer this question have focused on scene categorization or object detection, real-world scenes contain a wealth of information whose perceptual availability has yet to be explored. We compared image exposure thresholds in several tasks involving basic-level categorization or global- property classification. All thresholds were remarkably short: Observers achieved 75%-correct performance with presentations ranging from 19 to 67 ms, reaching maximum performance at about 100 ms. Global-property categorization was performed with significantly less presentation time than basic-level categorization, which suggests that there exists a time during early visual processing when a scene may be classified as, for example, a large space or navigable, but not yet as a mountain or lake. Comparing the relative availability of visual information reveals bottlenecks in the accumulation of meaning. Understanding these bottlenecks provides critical insight into the computations underlying rapid visual understanding.

How many pixels make an image?

The human visual system is remarkably tolerant to degradation in image resolution: human performance in scene categorization remains high no matter whether low-resolution images or multimegapixel images are used. This observation raises the question of how many pixels are required to form a meaningful representation of an image and identify the objects it contains. In this article, we show that very small thumbnail images at the spatial resolution of 32 × 32 color pixels provide enough information to identify the semantic category of real-world scenes. Most strikingly, this low resolution permits observers to report, with 80% accuracy, four to five of the objects that the scene contains, despite the fact that some of these objects are unrecognizable in isolation. The robustness of the information available at very low resolution for describing semantic content of natural images could be an important asset to explain the speed and efficiently at which the human brain comprehends the gist of visual scenes.