Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Refining image annotation using contextual relations between words
Proceedings of the 6th ACM international conference on Image and video retrieval
Using multiple segmentations for image auto-annotation
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
Enhancing image annotation by integrating concept ontology and text-based bayesian learning model
Proceedings of the 15th international conference on Multimedia
Semantic image classification using statistical local spatial relations model
Multimedia Tools and Applications
Improving Automatic Image Annotation Based on Word Co-occurrence
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
Image Emotional Classification Based on Color Semantic Description
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Exploring multimedia in a keyword space
MM '08 Proceedings of the 16th ACM international conference on Multimedia
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
MAMECTIS'08 Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
Semantics-preserving bag-of-words models for efficient image annotation
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Distance metric learning from uncertain side information with application to automated photo tagging
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Unsupervised Clustering of Clickthrough Data for Automatic Annotation of Multimedia Content
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Knowledge Based Image Annotation Refinement
Journal of Signal Processing Systems
Investigating visual feature extraction methods for image annotation
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Improved Resulted Word Counts Optimizer for Automatic Image Annotation Problem
Fundamenta Informaticae - Advances in Artificial Intelligence and Applications
Empirical investigations on benchmark tasks for automatic image annotation
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Web image annotation based on automatically obtained noisy training set
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
Image retrieval using Markov Random Fields and global image features
Proceedings of the ACM International Conference on Image and Video Retrieval
Automatic tag expansion using visual similarity for photo sharing websites
Multimedia Tools and Applications
Baselines for Image Annotation
International Journal of Computer Vision
An information-theoretic framework for semantic-multimedia retrieval
ACM Transactions on Information Systems (TOIS)
Semantics-preserving bag-of-words models and applications
IEEE Transactions on Image Processing
Context dependent SVMs for interconnected image network annotation
Proceedings of the international conference on Multimedia
Distance metric learning from uncertain side information for automated photo tagging
ACM Transactions on Intelligent Systems and Technology (TIST)
Mining social images with distance metric learning for automated image tagging
Proceedings of the fourth ACM international conference on Web search and data mining
Context-based support vector machines for interconnected image annotation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Semantics extraction from images
Knowledge-driven multimedia information extraction and ontology evolution
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MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Automatic refinement of keyword annotations for web image search
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Mining multiple visual appearances of semantics for image annotation
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Logistic regression of generic codebooks for semantic image retrieval
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Improved Resulted Word Counts Optimizer for Automatic Image Annotation Problem
Fundamenta Informaticae - Advances in Artificial Intelligence and Applications
Random forest for image annotation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Applying a lightweight iterative merging chinese segmentation in web image annotation
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Explicit context-aware kernel map learning for image annotation
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
Learning to Recommend Descriptive Tags for Questions in Social Forums
ACM Transactions on Information Systems (TOIS)
Automated content labeling using context in email
Proceedings of the 17th International Conference on Management of Data
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We introduce a new method to automatically annotate and retrieve images using a vocabulary of image semantics. The novel contributions include a discriminant formulation of the problem, a multiple instance learning solution that enables the estimation of concept probability distributions without prior image segmentation, and a hierarchical description of the density of each image class that enables very efficient training. Compared to current methods of image annotation and retrieval, the one now proposed has significantly smaller time complexity and better recognition performance. Specifically, its recognition complexity is O(CxR), where C is the number of classes (or image annotations) and R is the number of image regions, while the best results in the literature have complexity O(TxR), where T is the number of training images. Since the number of classes grows substantially slower than that of training images, the proposed method scales better during training, and processes test images faster. This is illustrated through comparisons in terms of complexity, time, and recognition performance with current state-of-the-art methods.