Building the gist of a scene: the role of global image features in recognition.

[1]  Antonio Torralba,et al.  Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. , 2006, Psychological review.

[2]  John K. Tsotsos,et al.  Neurobiology of Attention , 2005 .

[3]  Michelle R. Greene,et al.  Better to run than to hide: The time course of naturalistic scene decisions , 2005 .

[4]  Paul T. Sowden,et al.  The use of visual information in natural scenes , 2005 .

[5]  A. Oliva,et al.  Diagnostic colours contribute to the early stages of scene categorization: Behavioural and neurophysiological evidence , 2005 .

[6]  Olivier R. Joubert,et al.  How long to get to the “gist” of real-world natural scenes? , 2005 .

[7]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Thomas Serre,et al.  Object recognition with features inspired by visual cortex , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Stella X. Yu,et al.  Segmentation induced by scale invariance , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[10]  V. Maljkovic,et al.  Short-term memory for scenes with affective content. , 2005, Journal of vision.

[11]  Tony Lindeberg,et al.  Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention , 1993, International Journal of Computer Vision.

[12]  Abel G. Oliva,et al.  Gist of a scene , 2005 .

[13]  Soon Chun Siong,et al.  Cortical Areas Involved in Object, Background, and Object-Background Processing Revealed with Functional Magnetic Resonance Adaptation , 2004, The Journal of Neuroscience.

[14]  Jitendra Malik,et al.  When is scene identification just texture recognition? , 2004, Vision Research.

[15]  Bernt Schiele,et al.  A Semantic Typicality Measure for Natural Scene Categorization , 2004, DAGM-Symposium.

[16]  M. Bar.  Visual objects in context , 2004, Nature Reviews Neuroscience.

[17]  K. Grill-Spector,et al.  The human visual cortex. , 2004, Annual review of neuroscience.

[18]  Ravi S. Menon,et al.  Behavioral and Neuroimaging Evidence for a Contribution of Color and Texture Information to Scene Classification in a Patient with Visual Form Agnosia , 2004, Journal of Cognitive Neuroscience.

[19]  A. Torralba,et al.  Sharing features: efficient boosting procedures for multiclass object detection , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[20]  M. Potter,et al.  Pictorial and conceptual representation of glimpsed pictures. , 2004, Journal of experimental psychology. Human perception and performance.

[21]  M. Chun,et al.  The Neural Fate of Consciously Perceived and Missed Events in the Attentional Blink , 2004, Neuron.

[22]  Stefano Soatto,et al.  Deformotion: Deforming Motion, Shape Average and the Joint Registration and Approximation of Structures in Images , 2003, International Journal of Computer Vision.

[23]  Antonio Torralba,et al.  Contextual Priming for Object Detection , 2003, International Journal of Computer Vision.

[24]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[25]  L. Chalupa,et al.  The visual neurosciences , 2004 .

[26]  Michael L. Mack,et al.  Identifying the Perceptual Dimensions of Visual Complexity of Scenes , 2004 .

[27]  Antonio Torralba,et al.  Top-down control of visual attention in object detection , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[28]  Antonio Torralba,et al.  Modeling global scene factors in attention. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[29]  M. Bar,et al.  Cortical Analysis of Visual Context , 2003, Neuron.

[30]  A. Treisman,et al.  Representation of statistical properties , 2003, Vision Research.

[31]  Antonio Torralba,et al.  Statistics of natural image categories , 2003, Network.

[32]  Antonio Torralba,et al.  Scene-Centered Description from Spatial Envelope Properties , 2002, Biologically Motivated Computer Vision.

[33]  Antonio Torralba,et al.  Depth Estimation from Image Structure , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Jitendra Malik,et al.  Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Michel Vidal-Naquet,et al.  Visual features of intermediate complexity and their use in classification , 2002, Nature Neuroscience.

[36]  P. Perona,et al.  Rapid natural scene categorization in the near absence of attention , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[37]  Helga C. Arsenio,et al.  An unbinding problem? The disintegration of visible, previously attended objects does not attract attention. , 2002, Journal of vision.

[38]  M. Tarr,et al.  Visual Object Recognition , 1996, ISTCS.

[39]  David A. Forsyth,et al.  Learning the semantics of words and pictures , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[40]  J. Lund,et al.  Compulsory averaging of crowded orientation signals in human vision , 2001, Nature Neuroscience.

[41]  S. Thorpe,et al.  The Time Course of Visual Processing: From Early Perception to Decision-Making , 2001, Journal of Cognitive Neuroscience.

[42]  D. Ariely.  Seeing Sets: Representation by Statistical Properties , 2001, Psychological science.

[43]  Jeffrey S. Perry,et al.  Edge co-occurrence in natural images predicts contour grouping performance , 2001, Vision Research.

[44]  A. Oliva,et al.  Diagnostic Colors Mediate Scene Recognition , 2000, Cognitive Psychology.

[45]  Ronald A. Rensink.  The Dynamic Representation of Scenes , 2000 .

[46]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[47]  Kenji Kawano,et al.  Global and fine information coded by single neurons in the temporal visual cortex , 1999, Nature.

[48]  Russell A. Epstein,et al.  The Parahippocampal Place Area Recognition, Navigation, or Encoding? , 1999, Neuron.

[49]  A. Oliva,et al.  Dr. Angry and Mr. Smile: when categorization flexibly modifies the perception of faces in rapid visual presentations , 1999, Cognition.

[50]  C. Heaps,et al.  Similarity and Features of Natural Textures , 1999 .

[51]  Anil K. Jain,et al.  On image classification: city images vs. landscapes , 1998, Pattern Recognit..

[52]  R. Kimchi.  Uniform connectedness and grouping in the perceptual organization of hierarchical patterns. , 1998, Journal of experimental psychology. Human perception and performance.

[53]  Nancy Kanwisher,et al.  A cortical representation of the local visual environment , 1998, Nature.

[54]  Edward E. Smith,et al.  An Invitation to cognitive science , 1997 .

[55]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[56]  A. Oliva,et al.  Coarse Blobs or Fine Edges? Evidence That Information Diagnosticity Changes the Perception of Complex Visual Stimuli , 1997, Cognitive Psychology.

[57]  W. Epstein,et al.  Priming Spatial Layout of Scenes , 1997 .

[58]  Roland Baddeley,et al.  The Correlational Structure of Natural Images and the Calibration of Spatial Representations , 1997, Cogn. Sci..

[59]  W. Eric L. Grimson,et al.  Configuration based scene classification and image indexing , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[60]  J. Wolfe,et al.  Preattentive Object Files: Shapeless Bundles of Basic Features , 1997, Vision Research.

[61]  J R Lishman,et al.  Role of coarse and fine spatial information in face and object processing. , 1996, Journal of experimental psychology. Human perception and performance.

[62]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[63]  Denis Fize,et al.  Speed of processing in the human visual system , 1996, Nature.

[64]  A. Oliva,et al.  From Blobs to Boundary Edges: Evidence for Time- and Spatial-Scale-Dependent Scene Recognition , 1994 .

[65]  A. Ravishankar Rao,et al.  Identifying High Level Features of Texture Perception , 1993, CVGIP Graph. Model. Image Process..

[66]  R. Kimchi.  Primacy of wholistic processing and global/local paradigm: a critical review. , 1992, Psychological bulletin.

[67]  J R Lishman,et al.  Temporal Integration of Spatially Filtered Visual Images , 1992, Perception.

[68]  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.

[69]  R. Watt.  Scanning from coarse to fine spatial scales in the human visual system after the onset of a stimulus. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[70]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

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

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

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

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

[75]  D. Hubel,et al.  Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.