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
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
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
Learning the Semantics of Images by Using Unlabeled Samples
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Evaluating the impact of selection noise in community-based web search
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Enhancing image annotation by integrating concept ontology and text-based bayesian learning model
Proceedings of the 15th international conference on Multimedia
Automatic image annotation via local multi-label classification
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
A discrete direct retrieval model for image and video retrieval
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
New approach for hierarchical classifier training and multi-level image annotation
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Image retrieval using Markov Random Fields and global image features
Proceedings of the ACM International Conference on Image and Video Retrieval
Mining multiple visual appearances of semantics for image annotation
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Semantic context based refinement for news video annotation
Multimedia Tools and Applications
Effective automatic image annotation via integrated discriminative and generative models
Information Sciences: an International Journal
Hi-index | 0.00 |
We propose a novel Bayesian learning framework of hierarchical mixture model by incorporating prior hierarchical knowledge into concept representations of multi-level concept structures in images. Characterizing image concepts by mixture models is one of the most effective techniques in automatic image annotation (AIA) for concept-based image retrieval. However it also poses problems when large-scale models are needed to cover the wide variations in image samples. To alleviate the potential difficulties arising in estimating too many parameters with insufficient training images, we treat the mixture model parameters as random variables characterized by a joint conjugate prior density of the mixture model parameters. This facilitates a statistical combination of the likelihood function of the available training data and the prior density of the concept parameters into a well-defined posterior density whose parameters can now be estimated via a maximum a posteriori criterion. Experimental results on the Corel image dataset with a set of 371 concepts indicate that the proposed Bayesian approach achieved a maximum F1 measure of 0.169, which outperforms many state-of-the-art AIA algorithms.