Multimedia Systems - Special issue on content-based retrieval
Support vector machine pairwise classifiers with error reduction for image classification
MULTIMEDIA '01 Proceedings of the 2001 ACM workshops on Multimedia: multimedia information retrieval
Image Classification and Retrieval Based on Wavelet-SOM
DANTE '99 Proceedings of the 1999 International Symposium on Database Applications in Non-Traditional Environments
On Image Classification: City vs. Landscape
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Configuration based scene classification and image indexing
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Supervised Image Classification by SOM Activity Map Comparison
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
SAFIRE: towards standardized semantic rich image annotation
AMR'06 Proceedings of the 4th international conference on Adaptive multimedia retrieval: user, context, and feedback
Hidden semantic concept discovery in region based image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Image classification for content-based indexing
IEEE Transactions on Image Processing
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Recent approaches in Automatic Image Annotation (AIA) try to combine the expressiveness of natural language queries with approaches to minimize the manual effort for image annotation. The main idea is to infer the annotations of unseen images using a small set of manually annotated training examples. However, typically these approaches suffer from low correlation between the globally assigned annotations and the local features used to obtain annotations automatically. In this paper we propose a framework to support image annotations based on a visual dictionary that is created automatically using a set of locally annotated training images. We designed a segmentation and annotation interface to allow for easy annotation of the traing data. In order to provide a framework that is easily extendable and reusable we make broad use of the MPEG-7 standard.