Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
The Journal of Machine Learning Research
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
Image annotations by combining multiple evidence & wordNet
Proceedings of the 13th annual ACM international conference on Multimedia
Automatic image annotation and retrieval using weighted feature selection
Multimedia Tools and Applications
Putting Objects in Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Computational Linguistics
iCLEF 2006 Overview: searching the flickr WWW photo-sharing repository
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
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Certain components in images can be recognized with high accuracy, for example, backgrounds such as leaves, grass, snow, sky, water. These components provide the human eye with context for identifying items in the foreground. Likewise for the machine, the identification of background should help in the recognition of foreground objects. But, in this case, the computer needs explicit lists of object and background co-occurrence probabilities. We examine two ways of deriving estimates of these a priori object co-occurrence probabilities: using an online social network of people storing annotated images, FlickR; and using variations on co-occurrence frequencies in natural language text. We show that the object co-occurrence probabilities derived from both sources are very similar. The possibility of using non-image derived semantic knowledge drawn from text processing for object recognition opens up possibilities of mining a priori probabilities for a much wider class of objects than those found in manually annotated collections.