WordNet: a lexical database for English
Communications of the ACM
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
SVM binary classifier ensembles for image classification
Proceedings of the tenth international conference on Information and knowledge management
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Mining images on semantics via statistical learning
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Learning Hierarchical Models of Scenes, Objects, and Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Refining image annotation using contextual relations between words
Proceedings of the 6th ACM international conference on Image and video retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Expert Systems with Applications: An International Journal
Constructing Category Hierarchies for Visual Recognition
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Knowledge Based Image Annotation Refinement
Journal of Signal Processing Systems
A Bayesian network-based framework for semantic image understanding
Pattern Recognition
Semantic label sharing for learning with many categories
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
What does classifying more than 10,000 image categories tell us?
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Attribute-based transfer learning for object categorization with zero/one training example
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Semantic hierarchies for image annotation: A survey
Pattern Recognition
Large-scale image classification: Fast feature extraction and SVM training
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Mining Multilevel Image Semantics via Hierarchical Classification
IEEE Transactions on Multimedia
Multitraining Support Vector Machine for Image Retrieval
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Structured Max-Margin Learning for Inter-Related Classifier Training and Multilabel Image Annotation
IEEE Transactions on Image Processing
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Learning similarity measure for natural image retrieval with relevance feedback
IEEE Transactions on Neural Networks
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A structural learning algorithm is developed in this paper to achieve more effective training of large numbers of inter-related classifiers for supporting large-scale image classification and annotation. A visual concept network is constructed for characterizing the inter-concept visual correlations intuitively and determining the inter-related learning tasks automatically in the visual feature space rather than in the label space. By partitioning large numbers of object classes and image concepts into a set of groups according to their inter-concept visual correlations, the object classes and image concepts in the same group will share similar visual properties and their classifiers are strongly inter-related while the object classes and image concepts in different groups will contain various visual properties and their classifiers can be trained independently. By leveraging the inter-concept visual correlations for inter-related classifier training, our structural learning algorithm can train the inter-related classifiers jointly rather than independently, which can enhance their discrimination power significantly. Our experiments have also provided very positive results on large-scale image classification and annotation.