From Blobs to Boundary Edges: Evidence for Time- and Spatial-Scale-Dependent Scene Recognition

In very fast recognition tasks, scenes are identified as fast as isolated objects How can this efficiency be achieved, considering the large number of component objects and interfering factors, such as cast shadows and occlusions? Scene categories tend to have distinct and typical spatial organizations of their major components If human perceptual structures were tuned to extract this information early in processing, a coarse-to-fine process could account for efficient scene recognition A coarse description of the input scene (oriented “blobs” in a particular spatial organization) would initiate recognition before the identity of the objects is processed We report two experiments that contrast the respective roles of coarse and fine information in fast identification of natural scenes The first experiment investigated whether coarse and fine information were used at different stages of processing The second experiment tested whether coarse-to-fine processing accounts for fast scene categorization The data suggest that recognition occurs at both coarse and fine spatial scales By attending first to the coarse scale, the visual system can get a quick and rough estimate of the input to activate scene schemas in memory, attending to fine information allows refinement, or refutation, of the raw estimate

[1]  J. Robson,et al.  Application of fourier analysis to the visibility of gratings , 1968, The Journal of physiology.

[2]  M. Potter Meaning in visual search. , 1975, Science.

[3]  M. Potter Short-term conceptual memory for pictures. , 1976, Journal of experimental psychology. Human learning and memory.

[4]  A. Watson,et al.  Patterns of temporal interaction in the detection of gratings , 1977, Vision Research.

[5]  D. Navon Forest before trees: The precedence of global features in visual perception , 1977, Cognitive Psychology.

[6]  A. Friedman Framing pictures: the role of knowledge in automatized encoding and memory for gist. , 1979, Journal of experimental psychology. General.

[7]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[8]  I. Biederman,et al.  Scene perception: Detecting and judging objects undergoing relational violations , 1982, Cognitive Psychology.

[9]  N Weisstein,et al.  Sharp targets are detected better against a figure, and blurred targets are detected better against a background. , 1983, Journal of experimental psychology. Human perception and performance.

[10]  G R Grice,et al.  Forest before trees? It depends where you look , 1983, Perception & psychophysics.

[11]  J Wilson,et al.  Spatial Frequency and Selective Attention to Local and Global Information , 1987, Perception.

[12]  H H Bülthoff,et al.  Integration of depth modules: stereo and shading. , 1988, Journal of the Optical Society of America. A, Optics and image science.

[13]  Shimon Ullman,et al.  Structural Saliency: The Detection Of Globally Salient Structures using A Locally Connected Network , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[14]  I. Biederman,et al.  Surface versus edge-based determinants of visual recognition , 1988, Cognitive Psychology.

[15]  Shimon Ullman,et al.  The computational study of vision , 1989 .

[16]  Nikos K Logothetis,et al.  The color-opponent and broad-band channels of the primate visual system , 1990, Trends in Neurosciences.

[17]  Peter De Graef,et al.  Scene-Context Effects and Models of Real-World Perception , 1992 .

[18]  J. Henderson Object identification in context: the visual processing of natural scenes. , 1992, Canadian journal of psychology.

[19]  K. Rayner Eye movements and visual cognition : scene perception and reading , 1992 .