Hostname: page-component-745bb68f8f-grxwn Total loading time: 0 Render date: 2025-01-11T21:59:30.802Z Has data issue: false hasContentIssue false
  • English
  • Français

How many pixels make an image?

Published online by Cambridge University Press:  01 January 2009

ANTONIO TORRALBA
ANTONIO TORRALBA*
Affiliation:
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts
*
*Address correspondence and reprint requests to: Antonio Torralba, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32-D432, 32 Vassar Street, Cambridge, MA 02139. E-mail: torralba@csail.mit.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

Keywords

Scene recognitionObject segmentationGistThumbnailsNatural imagesBlobs
Type
Natural Scene Statistics and Natural Tasks
Information
Visual Neuroscience , Volume 26 , Issue 1 , January 2009 , pp. 123 - 131
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bachmann, T. (1991). Identification of spatially quantized tachistoscopic images of faces: How many pixels does it take to carry identity? European Journal of Cognitive Psychology 3, 85–103.CrossRefGoogle Scholar
Bar, M. (2004). Visual objects in context. Nature Neuroscience Reviews 5, 617–629.CrossRefGoogle ScholarPubMed
Bar, M. (2007). The proactive brain: Using analogies and associations to generate predictions. Trends in Cognitive Sciences 11, 280–289.CrossRefGoogle ScholarPubMed
Castelhano, M.S. & Henderson, J.M. (2008). The influence of color on perception of scene gist. Journal of Experimental Psychology: Human Perception and Performance 34, 660–675.Google ScholarPubMed
Chandler, D.M. & Field, D.J. (2006). Estimates of the information content and dimensionality of natural scenes from proximity distributions. Journal of the Optical Society of America. A, Optics, Image Science, and Vision 24, 922–941.CrossRefGoogle Scholar
Fei-Fei, L., Iyer, A., Koch, C. & Perona, P. (2007). What do we perceive in a glance of a real-world scene? Journal of Vision 7(1), 1–29.CrossRefGoogle Scholar
Friedman, A. (1979). Framing pictures: The role of knowledge in automatized encoding and memory of gist. Journal of Experimental Psychology: General 108, 316–355.CrossRefGoogle ScholarPubMed
Goffaux, V., Jacques, C., Mouraux, A., Oliva, A., Rossion, B. & Schyns, P.G. (2005). Diagnostic colors contribute to early stages of scene categorization: Behavioral and neurophysiological evidences. Visual Cognition 12, 878–892.CrossRefGoogle Scholar
Greene, M.R. & Oliva, A. (2009). Recognition of natural scenes from global properties: Seeing the forest without representing the trees. Cognitive Psychology 58(2), 137–179.CrossRefGoogle ScholarPubMed
Harmon, L.D. & Julesz, B. (1973). Masking in visual recognition: Effects of two-dimensional filtered noise. Science 180, 1194–1197.CrossRefGoogle ScholarPubMed
Intraub, H. (1981). Rapid conceptual identification of sequentially presented pictures. Journal of Experimental Psychology: Human Perception and Performance 7, 604–610.Google Scholar
Joubert, O., Rousselet, G., Fize, D. & Fabre-Thorpe, M. (2007). Processing scene context: Fast categorization and object interference. Vision Research 47, 3286–3297.CrossRefGoogle ScholarPubMed
Klein, S.A. (2001). Measuring, estimating, and understanding the psychometric function: A commentary. Perception & Psychophysics 63, 1421–1455.CrossRefGoogle ScholarPubMed
Lee, A.B., Pedersen, K.S. & Mumford, D. (2003). The nonlinear statistics of high-contrast patches in natural images. International Journal of Computer Vision 54(1–3), 83–103.CrossRefGoogle Scholar
Oliva, A. & Schyns, P.G. (1997). Coarse blobs or fine edges? Evidence that information diagnosticity changes the perception of complex visual stimuli. Cognitive Psychology 34, 72–107.CrossRefGoogle ScholarPubMed
Oliva, A. (2005). Gist of the scene. In The Encyclopedia of Neurobiology of Attention, ed. Itti, L., Rees, G. & Tsotsos, J.K., pp. 251–256. San Diego, CA: Elsevier.CrossRefGoogle Scholar
Oliva, A. & Schyns, P.G. (2000). Diagnostic colors mediate scene recognition. Cognitive Psychology 41, 176–210.CrossRefGoogle ScholarPubMed
Oliva, A. & Torralba, A. (2001). Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42(3), 145–175.CrossRefGoogle Scholar
Oliva, A. & Torralba, A. (2007). The role of context in object recognition. Trends in Cognitive Sciences 11, 520–527.CrossRefGoogle ScholarPubMed
Olshausen, B.A., Anderson, C.H. & Van Essen, D.C. (1993). A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information. Journal of Neuroscience 13, 4700–4719.CrossRefGoogle ScholarPubMed
Olshausen, B.A. & Field, D.J. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609.CrossRefGoogle ScholarPubMed
Potter, M.C. (1975). Meaning in visual scenes. Science 187, 965–966.CrossRefGoogle Scholar
Renninger, L.W. & Malik, J. (2004). When is scene recognition just texture recognition? Vision Research 44, 2301–2311.CrossRefGoogle ScholarPubMed
Rousselet, G.A., Joubert, O.R. & Fabre-Thorpe, M. (2005). How long to get to the “gist” of real-world natural scenes? Visual Cognition 12, 852–877.CrossRefGoogle Scholar
Russell, B., Torralba, A., Murphy, K. & Freeman, W.T. (2008). LabelMe: A database and web-based tool for image annotation. International Journal of Computer Vision 77(3), 157–173.CrossRefGoogle Scholar
Schyns, P.G. & Oliva, A. (1994). From blobs to boundary edges: Evidence for time- and spatial-scale-dependent scene recognition. Psychological Science 5, 195–200.CrossRefGoogle Scholar
Schyns, P.G. & Oliva, A. (1997). Flexible, diagnostically-driven, rather than fixed, perceptually determined scale selection in scene and face recognition. Perception 26, 1027–1038.CrossRefGoogle Scholar
Serre, T., Oliva, A. & Poggio, T.A. (2007). A feedforward architecture accounts for rapid categorization. Proceedings of the National Academy of Sciences 104, 6424–6429.CrossRefGoogle ScholarPubMed
Sinha, P., Balas, B.J., Ostrovsky, Y. & Russell, R. (2006). Face recognition by humans: 19 results all computer vision researchers should know about. Proceedings of the IEEE 94 (No. 11), 1948–1962.CrossRefGoogle Scholar
Thorpe, S., Fize, D. & Marlot, C. (1996). Speed of processing in the human visual system. Nature 381, 520–522.CrossRefGoogle ScholarPubMed
Torralba, A., Fergus, R. & Freeman, W.T. (2008). 80 million tiny images: A large dataset for non-parametric object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(11), 1958–1970.CrossRefGoogle ScholarPubMed
Torralba, A., Oliva, A., Castelhano, M. & Henderson, J.M. (2006). Contextual guidance of attention in natural scenes: The role of global features on object search. Psychological Review 113, 766–786.CrossRefGoogle ScholarPubMed
VanRullen, R. & Thorpe, S.J. (2001 a). Rate coding versus temporal order coding: What the retinal ganglion cells tell the visual cortex. Neural Computation 13, 1255–1283.Google Scholar
VanRullen, R. & Thorpe, S.J. (2001 b). The time course of visual processing: From early perception to decision making. Journal of Cognitive Neuroscience 13, 454–461.CrossRefGoogle ScholarPubMed
Wolfe, J.M. (1998). Visual memory: What do you know about what you saw? Current Biology 8, R303–R304.CrossRefGoogle Scholar
Wurm, L.H., Legge, G.E., Isenberg, L.M. & Luebker, A. (1993). Color improves object recognition in normal and low vision. Journal of Experimental Psychology: Human Perception and Performance 19, 899–911.Google ScholarPubMed