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
Effective automatic image annotation via a coherent language model and active learning
Proceedings of the 12th annual ACM international conference on Multimedia
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
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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
Image annotations by combining multiple evidence & wordNet
Proceedings of the 13th annual ACM international conference on Multimedia
An adaptive graph model for automatic image annotation
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 6th ACM international conference on Image and video retrieval
Information-theoretic semantic multimedia indexing
Proceedings of the 6th ACM international conference on Image and video retrieval
Correlative multi-label video annotation
Proceedings of the 15th international conference on Multimedia
Enhancing image annotation by integrating concept ontology and text-based bayesian learning model
Proceedings of the 15th international conference on Multimedia
Dual cross-media relevance model for image annotation
Proceedings of the 15th international conference on Multimedia
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
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Statistical models for text query-based image retrieval
Statistical models for text query-based image retrieval
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Markov random fields and spatial information to improve automatic image annotation
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Overview of the CLEF 2009 large-scale visual concept detection and annotation task
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
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
Automated image annotation using global features and robust nonparametric density estimation
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Automatic image annotation based on wordnet and hierarchical ensembles
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
An energy-based model for region-labeling
Computer Vision and Image Understanding
Exploiting time in automatic image tagging
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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In this paper, we propose a direct image retrieval framework based on Markov Random Fields (MRFs) that exploits the semantic context dependencies of the image. The novelty of our approach lies in the use of different kernels in our non-parametric density estimation together with the utilisation of configurations that explore semantic relationships among concepts at the same time as low-level features, instead of just focusing on correlation between image features like in previous formulations. Hence, we introduce several configurations and study which one achieve the best performance. Results are presented for two datasets, the usual benchmark Corel 5k and the collection proposed by the 2009 edition of the ImageCLEF campaign. We observe that, using MRFs, performance increases significantly depending on the kernel used in the density estimation for the two datasets. With respect to the the language model, best results are obtained for the configuration that exploits dependencies between words together with dependencies between words and visual features. For the Corel 5k dataset, our best result corresponds to a mean average precision of 0.32, which compares favourably with the highest value ever obtained, 0.35, achieved by Makadia et al. [22] albeit with different features. For the ImageCLEF09 collection, we obtained 0.32, as mean average precision.