WordNet: a lexical database for English
Communications of the ACM
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Art of Computer Programming, 2nd Ed. (Addison-Wesley Series in Computer Science and Information
The Art of Computer Programming, 2nd Ed. (Addison-Wesley Series in Computer Science and Information
Texture Features and Learning Similarity
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
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
AnnoSearch: Image Auto-Annotation by Search
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Automatic image annotation via local multi-label classification
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Collaborative learning for image and video annotation
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
A novel approach for filtering junk images from google search results
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
New approach for hierarchical classifier training and multi-level image annotation
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Multimedia data mining: state of the art and challenges
Multimedia Tools and Applications
Efficient large-scale image data set exploration: visual concept network and image summarization
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
Semantic hierarchies for image annotation: A survey
Pattern Recognition
Methods for automatic and assisted image annotation
Multimedia Tools and Applications
Building semantic hierarchies faithful to image semantics
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
A kernel-based framework for image collection exploration
Journal of Visual Languages and Computing
Effective automatic image annotation via integrated discriminative and generative models
Information Sciences: an International Journal
Image categorization using a semantic hierarchy model with sparse set of salient regions
Frontiers of Computer Science: Selected Publications from Chinese Universities
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In this paper, a hierarchical classification framework has been proposed for bridging the semantic gap effectively and achieving multi-level image annotation automatically. First, the semantic gap between the low-level computable visual features and users' real information needs is partitioned into four smaller gaps, and multiple approachesallare proposed to bridge these smaller gaps more effectively. To learn more reliable contextual relationships between the atomic image concepts and the co-appearances of salient objects, a multi-modal boosting algorithm is proposed. To enable hierarchical image classification and avoid inter-level error transmission, a hierarchical boosting algorithm is proposed by incorporating concept ontology and multi-task learning to achieve hierarchical image classifier training with automatic error recovery. To bridge the gap between the computable image concepts and the users' real information needs, a novel hyperbolic visualization framework is seamlessly incorporated to enable intuitive query specification and evaluation by acquainting the users with a good global view of large-scale image collections. Our experiments on large-scale image databases have also obtained very positive results.