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
Regularizing translation models for better automatic image annotation
Proceedings of the thirteenth ACM international conference on Information and knowledge management
GCap: Graph-based Automatic Image Captioning
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 9 - Volume 09
Formulating Semantic Image Annotation as a Supervised Learning Problem
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Multiple Class Machine Learning Approach for an Image Auto-Annotation Problem
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
Fast image auto-annotation with discretized feature distance measures
Machine Graphics & Vision International Journal
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
A Study of Quality Issues for Image Auto-Annotation With the Corel Dataset
IEEE Transactions on Circuits and Systems for Video Technology
Improved Resulted Word Counts Optimizer for Automatic Image Annotation Problem
Fundamenta Informaticae - Advances in Artificial Intelligence and Applications
PATSI: photo annotation through finding similar images with multivariate Gaussian models
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part II
Image similarities on the basis of visual content: an attempt to bridge the semantic gap
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Improved Resulted Word Counts Optimizer for Automatic Image Annotation Problem
Fundamenta Informaticae - Advances in Artificial Intelligence and Applications
Content-based annotation and classification framework: a general multi-purpose approach
Proceedings of the 17th International Database Engineering & Applications Symposium
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One of major problems in image auto-annotation is the difference between the expected word counts vector and the resulted word counts vector. This paper presents a new approach to automatic image annotation-an algorithm called resulted word counts optimizer which is an extension to existing methods. An ideal annotator is defined in terms of recall quality measure. On the basis of the ideal annotator an optimization criterion is defined. It allows to reduce the difference between resulted and expected word counts vectors. The proposed algorithm can be used with various image auto-annotation algorithms because its generic nature. Additionally, it does not increase the computational complexity of the original annotation method processing phase. It changes output word probabilities according to a pre-calculated vector of correction coefficients.