Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
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
The Journal of Machine Learning Research
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Semantic Modeling of Natural Scenes for Content-Based Image Retrieval
International Journal of Computer Vision
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Exploiting spatial context constraints for automatic image region annotation
Proceedings of the 15th international conference on Multimedia
Combining global and local information for knowledge-assisted image analysis and classification
EURASIP Journal on Advances in Signal Processing
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Exploiting Spatial Context in Image Region Labelling Using Fuzzy Constraint Reasoning
WIAMIS '08 Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Labelling Image Regions Using Wavelet Features and Spatial Prototypes
SAMT '08 Proceedings of the 3rd International Conference on Semantic and Digital Media Technologies: Semantic Multimedia
International Journal of Computer Vision
Particle Swarm Model Selection
The Journal of Machine Learning Research
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
Exploiting term co-occurrence for enhancing automated image annotation
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Image retrieval using Markov Random Fields and global image features
Proceedings of the ACM International Conference on Image and Video Retrieval
Semi-supervised learning for image annotation based on conditional random fields
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Multimodal indexing based on semantic cohesion for image retrieval
Information Retrieval
Acute leukemia classification by ensemble particle swarm model selection
Artificial Intelligence in Medicine
The effectiveness of image features based on fractal image coding for image annotation
Expert Systems with Applications: An International Journal
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This paper introduces an energy-based model (EBM) for region labeling that takes advantage of both context and semantics present in segmented images.The proposed method refines the output of multiclass classification methods that are based on the one-vs-all (OVA) formulation. Intuitively, the EBM maximizes the semantic cohesion among labels assigned to neighboring regions; that is, a tradeoff between label-association information and the predictions from the base classifier. Additionally, we study the suitability of OVA classification for the region labeling task. We report experimental results of our methods in 12 heterogeneous data sets that have been used for the evaluation of different tasks besides region labeling. On the one hand, our results reveal that the OVA approach offers an important potential of improvement in terms of labeling performance that can be exploited by refinement techniques similar to ours. On the other hand, experimental results show that our EBM improves the labeling provided by the base classifier. The EBM is highly efficient and it can be applied without modifications to different data sets. The heterogeneity of the considered databases shows the generality of our approach and its robustness to different scenarios. Our results are superior to other techniques that have been tested in the same collections. Furthermore, results on image retrieval show that the labels generated with our EBM can be helpful for annotation-based image retrieval.