Machine vision
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Role of Featural and Configural Information in Familiar and Unfamiliar Face Recognition
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A psychophysically plausible model for typicality ranking of natural scenes
ACM Transactions on Applied Perception (TAP)
Semantic Modeling of Natural Scenes for Content-Based Image Retrieval
International Journal of Computer Vision
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Image classification for content-based indexing
IEEE Transactions on Image Processing
Incorporating concept ontology into multi-level image indexing
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Horizon estimation: perceptual and computational experiments
Proceedings of the 7th Symposium on Applied Perception in Graphics and Visualization
Towards artificial systems: what can we learn from human perception?
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Exploiting Textons distributions on spatial hierarchy for scene classification
Journal on Image and Video Processing - Special issue on selected papers from multimedia modeling conference 2009
Improved spatial pyramid matching for image classification
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
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Categorization of scenes is a fundamental process of human vision that allows us to efficiently and rapidly analyze our surroundings. Several studies have explored the processes underlying human scene categorization, but they have focused on processing global image information. In this study, we present both psychophysical and computational experiments that investigate the role of local versus global image information in scene categorization. In a first set of human experiments, categorization performance is tested when only local or only global image information is present. Our results suggest that humans rely on local, region-based information as much as on global, configural information. In addition, humans seem to integrate both types of information for intact scene categorization. In a set of computational experiments, human performance is compared to two state-of-the-art computer vision approaches that have been shown to be psychophysically plausible and that model either local or global information. In addition to the influence of local versus global information, in a second series of experiments, we investigated the effect of color on the categorization performance of both the human observers and the computational model. Analysis of the human data suggests that color is an additional channel of perceptual information that leads to higher categorization results at the expense of increased reaction times in the intact condition. However, it does not affect reaction times when only local information is present. When color is removed, the employed computational model follows the relative performance decrease of human observers for each scene category and can thus be seen as a perceptually plausible model for human scene categorization based on local image information.